Collaborating with Marine Birds to Monitor the Physical Environment Within Coastal Marine Protected Areas
Bibliographic record
Abstract
Animal telemetry is maturing into a viable method for observing the ocean as it can be used to monitor both environmental conditions and biological metrics along the movement trajectories of marine animals. As part of the Cormorant Oceanography Project, we have augmented a biologging tag with an external fast response temperature sensor to collect ocean temperature profiles from the backs of foraging marine birds. Cormorants dive between 50 and 250+ times a day to forage for prey so they can provide hard-to-match temporal and spatial coverage of coastal ocean conditions within their foraging areas. We process tag measurements to obtain fundamental oceanographic data (e.g., temperature profiles, bottom soundings, surface current measurements). Together, we have tracked 17 marine bird species (including two Spheniscus penguins spp. and a sea duck), originating from 17 countries and foraging along the edges of all major oceans. Tagged birds’ distribution included 191 MPAs in 26 countries, offering a unique ocean monitoring method to complement more widely used methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".