Quantifying thermal cues that initiate mass emigrations in juvenile white sharks
Bibliographic record
Abstract
Abstract While the function of migration varies among species, environmental temperature is known to be one of the most important abiotic variables that drive animal migration; however, quantifying the thresholds and timing of the cues that influence a mass emigration is difficult, often due to lack of monitoring resolution, particularly for large, highly mobile species. We used acoustic telemetry tracking and high-resolution water temperature data over a relatively large spatial scale (5.5 km2) to identify and quantify a thermal threshold for mass emigration of juvenile white sharks. Sixteen tagged sharks were observed to initiate a search for warmer water within 10–12 hours of an upwelling event where water temperatures dropped below 14 °C. Eleven sharks traveled ~ 35 km away where they experienced similar cold temperatures before returning to the aggregation site within 24 hours. Five days following the upwelling event, most sharks emigrated from the site for the season. Quantifying movement patterns across different spatial and temporal scales is necessary to understand cues and thresholds influencing animal migration, which may be greatly affected by climate anomalies and climate change, resulting in potential impacts on the dynamics of local prey species, management, and conservation policy and practice.
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.
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".