Biologging in the global ocean observing system
Bibliographic record
Abstract
Miniature electronic data recorders and transmitters have revolutionized the way we study animals over the past decades, particularly marine animals at sea. But, very recently, animal-borne instruments have also been designed and implemented that provide in situ hydrographic data from parts of the oceans where little or no other data are currently available (even from beneath the ice in polar regions). Ocean data is delivered from animal-borne instruments via satellites in near real-time, which would enrich the Global Ocean Observing System if animal-borne instruments were deployed systematically. In the last 10 years, studies involving more than 10 countries (Australia, Brazil, Canada, France, Germany, Greenland, Norway, South Africa, UK, USA) have demonstrated how highly accurate oceanographic sensors, integrated into standard animal, biologging instruments, can provide data of equal or better quality than XBT/XCTD data. Here, we present some of the pioneering studies and demonstrate that we now have enough information for many marine species to predict where they will go – within reasonable limits. Thus, we can direct sampling effort to particularly interesting and productive regions and maximize data return. In the future, biologging could certainly play an important part in the Global Ocean Observing System, by providing complementary data to more traditional sampling technologies - especially in the high latitudes. This paper will make a core contribution to the Plenary Sessions 4A, 4B and 5A and will be relevant to 2A, 2B and 3A.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".