Example of Environmetrics format
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Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format
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Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format Example of Environmetrics format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Environmetrics — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Statistics and Probability #100 of 239 down down by 50 ranks
Ecological Modeling #22 of 32 down down by 4 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 176 Published Papers | 330 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 17/06/2020
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FAQ

Related Journals

open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 5.9
SJR: 5.062
SNIP: 4.015
open access Open Access
recommended Recommended

Oxford University Press

Quality:  
High
CiteRatio: 9.9
SJR: 3.599
SNIP: 2.056
open access Open Access

Springer

Quality:  
High
CiteRatio: 2.5
SJR: 1.083
SNIP: 1.281
open access Open Access

Springer

Quality:  
High
CiteRatio: 2.9
SJR: 1.151
SNIP: 1.392

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.039

23% from 2018

Impact factor for Environmetrics from 2016 - 2019
Year Value
2019 1.039
2018 1.351
2017 1.321
2016 1.532
graph view Graph view
table view Table view

1.9

10% from 2019

CiteRatio for Environmetrics from 2016 - 2020
Year Value
2020 1.9
2019 2.1
2018 2.5
2017 2.6
2016 2.6
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 23% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 10% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.68

5% from 2019

SJR for Environmetrics from 2016 - 2020
Year Value
2020 0.68
2019 0.645
2018 0.87
2017 1.014
2016 0.989
graph view Graph view
table view Table view

0.864

2% from 2019

SNIP for Environmetrics from 2016 - 2020
Year Value
2020 0.864
2019 0.882
2018 0.965
2017 0.882
2016 1.019
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 5% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Environmetrics

Guideline source: View

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Use of these names, trademarks and brands does not imply endorsement or affiliation. Disclaimer Notice

Wiley

Environmetrics

The official Journal of The International Environmetrics Society (TIES) Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitat...... Read More

Statistics and Probability

Ecological Modelling

Mathematics

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Last updated on
17 Jun 2020
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ISSN
1180-4009
i
Impact Factor
High - 1.189
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
apa
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Citation Type
Numbered
[25]
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Bibliography Example
Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

Journal Article DOI: 10.1002/ENV.3170050203
Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values†
Pentti Paatero1, Unto Tapper1
01 Jun 1994 - Environmetrics

Abstract:

A new variant ‘PMF’ of factor analysis is described. It is assumed that X is a matrix of observed data and σ is the known matrix of standard deviations of elements of X. Both X and σ are of dimensions n × m. The method solves the bilinear matrix problem X = GF + E where G is the unknown left hand factor matrix (scores) of dim... A new variant ‘PMF’ of factor analysis is described. It is assumed that X is a matrix of observed data and σ is the known matrix of standard deviations of elements of X. Both X and σ are of dimensions n × m. The method solves the bilinear matrix problem X = GF + E where G is the unknown left hand factor matrix (scores) of dimensions n × p, F is the unknown right hand factor matrix (loadings) of dimensions p × m, and E is the matrix of residuals. The problem is solved in the weighted least squares sense: G and F are determined so that the Frobenius norm of E divided (element-by-element) by σ is minimized. Furthermore, the solution is constrained so that all the elements of G and F are required to be non-negative. It is shown that the solutions by PMF are usually different from any solutions produced by the customary factor analysis (FA, i.e. principal component analysis (PCA) followed by rotations). Usually PMF produces a better fit to the data than FA. Also, the result of PF is guaranteed to be non-negative, while the result of FA often cannot be rotated so that all negative entries would be eliminated. Different possible application areas of the new method are briefly discussed. In environmental data, the error estimates of data can be widely varying and non-negativity is often an essential feature of the underlying models. Thus it is concluded that PMF is better suited than FA or PCA in many environmental applications. Examples of successful applications of PMF are shown in companion papers. read more read less

Topics:

Nonnegative matrix (59%)59% related to the paper, Matrix (mathematics) (55%)55% related to the paper, Non-negative matrix factorization (54%)54% related to the paper, Matrix norm (53%)53% related to the paper, Principal component analysis (52%)52% related to the paper
4,101 Citations
Journal Article DOI: 10.1002/ENV.514
Large scale wildlife monitoring studies: statistical methods for design and analysis
01 Mar 2002 - Environmetrics

Abstract:

Techniques for estimation of absolute abundance of wildlife populations have received a lot of attention in recent years. The statistical research has been focused on intensive small-scale studies. Recently, however, wildlife biologists have desired to study populations of animals at very large scales for monitoring purposes.... Techniques for estimation of absolute abundance of wildlife populations have received a lot of attention in recent years. The statistical research has been focused on intensive small-scale studies. Recently, however, wildlife biologists have desired to study populations of animals at very large scales for monitoring purposes. Population indices are widely used in these extensive monitoring programs because they are inexpensive compared to estimates of absolute abundance. A crucial underlying assumption is that the population index (C) is directly proportional to the population density (D). The proportionality constant, β, is simply the probability of ‘detection’ for animals in the survey. As spatial and temporal comparisons of indices are crucial, it is necessary to also assume that the probability of detection is constant over space and time. Biologists intuitively recognize this when they design rigid protocols for the studies where the indices are collected. Unfortunately, however, in many field studies the assumption is clearly invalid. We believe that the estimation of detection probability should be built into the monitoring design through a double sampling approach. A large sample of points provides an abundance index, and a smaller sub-sample of the same points is used to estimate detection probability. There is an important need for statistical research on the design and analysis of these complex studies. Some basic concepts based on actual avian, amphibian, and fish monitoring studies are presented in this article. Copyright © 2002 John Wiley & Sons, Ltd. read more read less

Topics:

Population (55%)55% related to the paper, Statistical power (51%)51% related to the paper
552 Citations
open accessOpen access Journal Article DOI: 10.1002/ENV.2221
Bayesian stable isotope mixing models
01 Sep 2013 - Environmetrics

Abstract:

In this paper, we review recent advances in stable isotope mixing models (SIMMs) and place them into an overarching Bayesian statistical framework, which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixture. The most widely used application is quantify... In this paper, we review recent advances in stable isotope mixing models (SIMMs) and place them into an overarching Bayesian statistical framework, which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixture. The most widely used application is quantifying the diet of organisms based on the food sources they have been observed to consume. At the centre of the multivariate statistical model we propose is a compositional mixture of the food sources corrected for various metabolic factors. The compositional component of our model is based on the isometric log-ratio transform. Through this transform, we can apply a range of time series and non-parametric smoothing relationships. We illustrate our models with three case studies based on real animal dietary behaviour. read more read less

Topics:

Bayesian hierarchical modeling (52%)52% related to the paper
477 Citations
open accessOpen access Journal Article DOI: 10.1002/ENV.785
Spatial Modelling Using a New Class of Nonstationary Covariance Functions.
Christopher J. Paciorek1, Mark J. Schervish1
01 Aug 2006 - Environmetrics

Abstract:

We introduce a new class of nonstationary covariance functions for spatial modelling. Nonstationary covariance functions allow the model to adapt to spatial surfaces whose variability changes with location. The class includes a nonstationary version of the Matern stationary covariance, in which the differentiability of the sp... We introduce a new class of nonstationary covariance functions for spatial modelling. Nonstationary covariance functions allow the model to adapt to spatial surfaces whose variability changes with location. The class includes a nonstationary version of the Matern stationary covariance, in which the differentiability of the spatial surface is controlled by a parameter, freeing one from fixing the differentiability in advance. The class allows one to knit together local covariance parameters into a valid global nonstationary covariance, regardless of how the local covariance structure is estimated. We employ this new nonstationary covariance in a fully Bayesian model in which the unknown spatial process has a Gaussian process (GP) prior distribution with a nonstationary covariance function from the class. We model the nonstationary structure in a computationally efficient way that creates nearly stationary local behavior and for which stationarity is a special case. We also suggest non-Bayesian approaches to nonstationary kriging.To assess the method, we use real climate data to compare the Bayesian nonstationary GP model with a Bayesian stationary GP model, various standard spatial smoothing approaches, and nonstationary models that can adapt to function heterogeneity. The GP models outperform the competitors, but while the nonstationary GP gives qualitatively more sensible results, it shows little advantage over the stationary GP on held-out data, illustrating the difficulty in fitting complicated spatial data. read more read less

Topics:

Covariance function (65%)65% related to the paper, Rational quadratic covariance function (62%)62% related to the paper, Covariance (61%)61% related to the paper, Gaussian process (56%)56% related to the paper, Kriging (52%)52% related to the paper
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424 Citations
Characterization of the trophic conditions of marine coastal waters with special reference to the NW Adriatic Sea: Proposal for a trophic scale, turbidity and generalized water quality index
R.A. Vollenweider1, Franco Giovanardi, G. Montanari, Attilio Rinaldi
01 May 1998 - Environmetrics

Abstract:

In pursuing earlier attempts to characterize the trophic state of inland waters, a new trophic index (TRIX) based on chlorophyll, oxygen saturation, mineral and total nitrogen and phosphorus, and applicable to coastal marine waters, is proposed. Numerically, the index is scaled from 0 to 10, covering a wide range of trophic c... In pursuing earlier attempts to characterize the trophic state of inland waters, a new trophic index (TRIX) based on chlorophyll, oxygen saturation, mineral and total nitrogen and phosphorus, and applicable to coastal marine waters, is proposed. Numerically, the index is scaled from 0 to 10, covering a wide range of trophic conditions from oligotrophy to eutrophy. Secchi disk transparency combined with chlorophyll, instead, defines a turbidity index (TRBIX) that serves as complementary water quality index. The two indices are combined in a general water quality index (GWQI). Statistical properties and application of these indices to specific situations are discussed on examples pertaining to the NW Adriatic Sea. It is believed that these indices will simplify and make comparison between different spatial and temporal trophic situations of marine coastal waters more consistent. © 1998 John Wiley & Sons, Ltd. read more read less

Topics:

Secchi disk (55%)55% related to the paper, Trophic level (55%)55% related to the paper, Trix (53%)53% related to the paper
417 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

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With SciSpace, you do not need a word template for Environmetrics.

It automatically formats your research paper to Wiley formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time comparison

Time taken to format a paper and Compliance with guidelines

Plagiarism Reports via Turnitin

SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.

Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

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Environmetrics format uses apa citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

1. Can I write Environmetrics in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Environmetrics guidelines and auto format it.

2. Do you follow the Environmetrics guidelines?

Yes, the template is compliant with the Environmetrics guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Environmetrics?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Environmetrics citation style.

4. Can I use the Environmetrics templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Environmetrics.

5. Can I use a manuscript in Environmetrics that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Environmetrics that you can download at the end.

6. How long does it usually take you to format my papers in Environmetrics?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Environmetrics.

7. Where can I find the template for the Environmetrics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Environmetrics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Environmetrics's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Environmetrics an online tool or is there a desktop version?

SciSpace's Environmetrics is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Environmetrics?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Environmetrics?”

11. What is the output that I would get after using Environmetrics?

After writing your paper autoformatting in Environmetrics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Environmetrics's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Environmetrics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Environmetrics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Environmetrics?

The 5 most common citation types in order of usage for Environmetrics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Environmetrics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Environmetrics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Environmetrics in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Environmetrics Endnote style according to Elsevier guidelines.

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Typset automatically formats your research paper to Environmetrics formatting guidelines and citation style.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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