Bibliographic record
Abstract
Pursuing your prey can be metabolically challenging at the best of times,but diving seals and sea lions have to do all that on a single lung full of air. Andreas Fahlman, from the Marine Mammal Research Unit at the University of British Columbia, is intrigued by the fine metabolic balance struck by foraging mammals. But when Fahlman and his colleagues began analysing the metabolic cost of individual dives during a foraging session, something didn't add up. The metabolic cost of each dive seemed to vary: while diving freely,the sea lion's first dive was the least costly and the last the most expensive. What was going on(p. 3573)?Knowing that sea lions rapidly replenish the majority of their oxygen during the first few minutes at the surface, but it takes much longer for the mammals to refill their haemoglobin oxygen stores completely, Fahlman wondered whether the sea lions were cutting corners and making subsequent dives on a partly filled tank to maximise the amount of time they spent foraging. If they were, that could account for the metabolic inconsistency; the first dive would look as if it cost less than all the subsequent dives, while the last dive(when the sea lions could finally restock completely) would appear to be the most costly.Fahlman realised that if his theory was correct, he could do away with the metabolic cost pattern if he detained the sea lions at the surface for sufficient time to completely refill their haemoglobin tanks. Then all dives should cost the same, as the sea lion would not develop an oxygen deficit during the first dive that was only repaid after the final dive. Teaming up with Caroline Svärd, David Rosen and Andrew Trites from the Marine Mammal Research Unit and David Jones from the University of British Columbia, Fahlman set about testing his theory.Working with a team of experienced animal trainers from the Vancouver Aquarium, the team prepared three sea lions to dive at a simulated foraging site. At the end of each dive, the animals swam to a respirometry dome at the surface where the team could monitor their oxygen levels as they replenished their oxygen supplies. During some of the dives, the team allowed the animals to make their own decisions when they returned to the foraging site. However,on other occasions, the team closed the door on the respirometry chamber as the animals surfaced, only allowing them to resume diving when they had completely refilled their oxygen stocks.Recording the amount of oxygen that the freely diving animals consumed each time they surfaced, Fahlman confirmed that their first dive always appeared to be the least costly and the last the most expensive. However, all of the dives of animals that were forced to sit at the surface and completely recharge their oxygen supplies appeared to cost the same. So rather than wasting valuable time at the surface completely refilling their oxygen supplies, the sea lions were choosing to dive on slightly empty tanks to maximise the amount of time spent pursuing tasty fish diners.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".