AN ALGORITHM TO IMPROVE GEOLOCATION POSITIONS USING SEA SURFACE TEMPERATURE AND DIVING DEPTH
Bibliographic record
Abstract
Abstract The at‐sea movement of marine mammals is an important component of their foraging ecology, but has been difficult to study. Geolocation timed‐data recorders (GLTDRs) estimate positions using measured light level to calculate day length and local noon. It is well known that these location estimates are imprecise (mean error of > 1°). Satellite telemetry generally provides a more accurate, but also more expensive means of monitoring movement. We evaluated the accuracy and precision of geolocation positions by comparing these locations with satellite data from Service Argos for eight free‐ranging gray seals (Halichoerns grypus) equipped with both a satellite‐linked data recorder (SDR) and a GLTDR. Geolocation positions differed by 1,026.0 ± 292.28 km from the corresponding Argos locations. We developed an algorithm to correct geolocation positions by comparing surface water temperature (ST) and dive depth collected by GLTDRs with existing sea‐surface temperature and bathymetry databases. The corrected positions were significantly closer (P < 0.025) to the Argos locations of these seals (94.2 ± 8.22 km). The original geolocation positions would have led to incorrect conclusions about the use of space by gray seals; however, the corrected positions can be reliably used to study the large‐scale spatial distribution of individuals.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".