A novel approach to quantifying the spatiotemporal behavior of instrumented grey seals used to sample the environment
Bibliographic record
Abstract
BACKGROUND: Paired with satellite location telemetry, animal-borne instruments can collect spatiotemporal data describing the animal's movement and environment at a scale relevant to its behavior. Ecologists have developed methods for identifying the area(s) used by an animal (e.g., home range) and those used most intensely (utilization distribution) based on location data. However, few have extended these models beyond their traditional roles as descriptive 2D summaries of point data. Here we demonstrate how the home range method, T-LoCoH, can be expanded to quantify collective sampling coverage by multiple instrumented animals using grey seals (Halichoerus grypus) equipped with GPS tags and acoustic transceivers on the Scotian Shelf (Atlantic Canada) as a case study. At the individual level, we illustrate how time and space-use metrics quantifying individual sampling coverage may be used to determine the rate of acoustic transmissions received. RESULTS: Grey seals collectively sampled an area of 11,308 km (2) and intensely sampled an area of 31 km (2) from June-December. The largest area sampled was in July (2094.56 km (2)) and the smallest area sampled occurred in August (1259.80 km (2)), with changes in sampling coverage observed through time. CONCLUSIONS: T-LoCoH provides an effective means to quantify changes in collective sampling effort by multiple instrumented animals and to compare these changes across time. We also illustrate how time and space-use metrics of individual instrumented seal movement calculated using T-LoCoH can be used to account for differences in the amount of time a bioprobe (biological sampling platform) spends in an area.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".