Introduction to the theme issue: Measuring physiology in free-living animals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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)’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it