Vocal recognition of individuals versus relative dominance rank among breeding male northern elephant seals (<i>Mirounga angustirostris</i>).
Bibliographic record
Abstract
Whether an animal truly recognizes an individual or a simple rule-based category (e.g., neighbor or offspring) has important behavioral and evolutionary implications such as the accuracy of social reciprocity. Many tests of individual recognition have focused on neighboring territorial males (“dear enemy” or “neighbor-stranger” recognition). Unfortunately the static territorial context of these tests, mostly with male songbirds, opens them to the criticism of being merely associative habituation. More dynamic mating assemblages, such as leks where vocally advertising animals encounter numerous others, are a potentially rich and largely untested alternative. The female defense polygyny practiced by male northern elephant seals during terrestrial breeding is such a dynamic system. To examine whether elephant seals were recognizing individuals or dominance categories we conducted a total of 53 playback experiments to 18 males at Año Nuevo State Reserve. Each playback was a series of threat calls assigned to four dominance conditions relative to the subject. Dominance was based on the outcomes of interactions among contesting male dyads. Responses were measured using three assays in situ and from video records of each experiment. Results thus far are consistent with the males not recognizing individuals but instead recognizing and responding appropriately to relative rank.
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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 | 0.000 |
| 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 | 0.000 | 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".