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Natalia L. Oliveira

Researcher at Carnegie Mellon University

Publications -  13
Citations -  70

Natalia L. Oliveira is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Contingency table & Likelihood-ratio test. The author has an hindex of 3, co-authored 12 publications receiving 32 citations. Previous affiliations of Natalia L. Oliveira include Johns Hopkins University School of Medicine & Federal University of São Carlos.

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Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction?

TL;DR: In this paper, the utility of these indicators from a forecasting perspective is studied. But the authors focus on five indicators, derived from medical insurance claims data, web search queries, and online survey responses, and ask whether their inclusion in a simple model leads to improved predictive accuracy relative to a similar model excluding it.
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An investigation onto Cd toxicity to freshwater microalga Chlorella sorokiniana in mixotrophy and photoautotrophy: A Bayesian approach.

TL;DR: It is shown that the reduced photosynthetic capacity under mixotrophy can end up reducing the release of oxygen gas, which can compromise the entire aquatic ecosystem.
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A discussion on significance indices for contingency tables under small sample sizes

TL;DR: In this paper, the authors define an accurate index for the celebrated hypotheses of homogeneity, independence, and Hardy-Weinberg equilibrium in contingency tables and define an exact LRT p-value as a benchmark to understand other indices.
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Copper and cadmium complexation by Cylindrospermopsis raciborskii exudates.

TL;DR: The present findings have important ecological implications, since the metal-ligand association is dynamic, and together with a diversity of ligands it can act as an environmental metal buffer, as a result, higher metal loads may be necessary for the detection of toxicity.
Posted ContentDOI

An Open Repository of Real-Time COVID-19 Indicators

TL;DR: The COVIDcast API as mentioned in this paper provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from de-identified medical claims data, massive online surveys, cell phone mobility data, and internet search trends.