Introduction to the theme issue: Measuring physiology in free-living animals
Bibliographic record
Abstract
Abstract By describing where animals go, biologging technologies (i.e. animal attached logging of biological variables with small electronic devices) have been used to document the remarkable athletic feats of wild animals since the 1940s. The rapid development and miniaturization of physiologging (i.e. logging of physiological variables such as heart rate, blood oxygen content, lactate, breathing frequency and tidal volume on devices attached to animals) technologies in recent times (e.g. devices that weigh less than 2 g mass that can measure electrical biopotentials for days to weeks) has provided astonishing insights into the physiology of free-living animals to document how and why wild animals undertake these extreme feats. Now, physiologging, which was traditionally hindered by technological limitations, device size, ethics and logistics, is poised to benefit enormously from the on-going developments in biomedical and sports wearables technologies. Such technologies are already improving animal welfare and yield in agriculture and aquaculture, but may also reveal future pathways for therapeutic interventions in human health by shedding light on the physiological mechanisms with which free-living animals undertake some of the most extreme and impressive performances on earth. This article is part of the theme issue ‘Measuring physiology in free-living animals (Part I)’.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.022 |
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".