Predation research with electronic tagging
Bibliographic record
Abstract
Predation is a fundamental aspect of ecology that drives ecosystem structure and function. A better understanding of predation can be facilitated by using electronic tags that log or transmit positions of predator or prey species in natural settings, however, there are special considerations that must be made to avoid biased estimates. We provide an overview of the tools available for studying predation with electronic tags including the tag types and analytical tools that can be used to identify where, when and how prey are killed by predators. We also discuss considerations for experimental design when studying predation using electronic tags, including how to minimize effects of capture and tagging procedures. Ongoing innovation and integration of sensors for tags will provide more detailed data about the performance of tagged predators and the fate of tagged prey. Where analysts can effectively resolve the timing of predation using state‐of‐the‐art tags and analytical tools, we foresee exciting advances in our understanding of animal demographics, evolutionary trajectories and management systems. Prospects to develop new tools and approaches for tracking predation while designing studies to more effectively limit bias are an important frontier for understanding ecosystems and addressing human–wildlife conflicts. Given great uncertainties about environmental change and intensifying conflicts between humans and predators, effective study designs integrating electronic tagging to study predation have a promising future in fundamental and applied ecology.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".