Deviance from truth: Telemetry location errors erode both precision and accuracy of habitat‐selection models
Bibliographic record
Abstract
ABSTRACT Radiocollars are an increasingly important tool in wildlife research. Yet, as with all remotely compiled data, measurement error is inherent in the technology. We directly compare radiocollars with low measurement error (Global Positioning System [GPS]) with radiocollars with high measurement error (Argos satellite). Specifically, we compare how differences in precision between GPS and Argos satellite technologies affect the estimation of resource selection functions (RSFs). We estimated RSF models from GPS and Argos satellite radiocollar data collected in December 2008 through April 2009 from wolves within the same pack in southwestern Alberta, Canada, and used Akaike's Information Criterion (AIC) to identify the most parsimonious models. In general, β coefficients were closer to zero and coefficients of variation were higher for models estimated using Argos data. But even more serious, AIC identified different top models between the Argos and GPS data sets because measurement error alone can induce attenuation bias, which leads to erroneous conclusions on selection of habitats. GPS radiocollar data were more precise and more accurate, resulting in RSF models that were a better representation of true habitat selection by each wolf pack. © 2013 The Wildlife Society.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".