Using thermosensitive radiotelemetry to document rest and activity in a semifossorial rodent
Bibliographic record
Abstract
Abstract We present a protocol for using temperature records from external thermosensitive radiotransmitters recorded by data‐logging receivers to identify bouts of rest and activity in mammals that sleep in a curled‐up posture. We illustrate the protocol using eastern chipmunks ( Tamias striatus ) sampled in the Ruiter Valley Land Trust near Mansonville, Quebec, Canada. Temperatures recorded at intervals of 12–45 min, when chipmunks were in their burrows as well as above ground, displayed sequences of warmer, stable temperatures (assumed to be produced by curled‐up, resting individuals) alternating with sequences of cooler, more variable temperatures (assumed to be produced by active individuals). By sampling points from typical sequences of rest and activity and using regression tree analyses to optimize the distinction between resting and active temperatures, we were able to define bouts of rest and activity. Torpor bouts were also identified by a distinct pattern of decreasing temperatures. Because curled‐up rest did not occur outside the burrow, we were able to validate the assignment of bouts by independent determination of chipmunk locations using handheld telemetry. The proportion of observations of chipmunks outside the burrow correctly classified as active and the proportion of chipmunks classified as resting that were correctly located in their burrows were both high (approx. 96%). For the numerous mammalian species that curl up to rest, continuously recorded thermosensitive telemetry has the potential to provide more precise and reliable data on rest and activity over longer periods, including the night and time in burrows or dens, and with less effort per individual than most alternative techniques. © 2011 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".