A case for restoring unity between biotelemetry and bio-logging to enhance animal tracking research
Bibliographic record
Abstract
Monitoring animals with electronic tags is an increasingly important tool for fundamental and applied ecological research. Based on the size of the system under study, the ability to recapture the animal, and research medium (e.g., aerial, freshwater, saltwater, terrestrial), tags selected may either log data in memory (bio-logging), transmit it to a receiver or satellite (biotelemetry), or have a hybrid design. Over time, we perceive that user groups are diverging based on increasing use of technology specific terms, favouring either bio-logging or biotelemetry. It is crucial to ensure that a divide does not become entrenched in the community because it will likely hinder efforts to advance field and analytical methods and reduce accessibility of animal tracking with electronic tags to early-career and new researchers. We discuss the context for this emerging problem and the evidence that this is manifesting within the scientific community. Finally, we suggest how the animal tracking community may work to address this issue to maximize the benefits of information transfer and integration between users of the two technologies.
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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 | 0.268 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.020 | 0.043 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".