Bibliographic record
Abstract
Sperm whale society is structured into clans that are primarily distinguished by vocal dialects, which may be symbolic markers of clan identity. However, clans also differ in non-vocal behaviour. These distinctive behaviours, as well as clan membership itself, are learned socially, largely within matrilines. The clans can contain thousands of whales and span thousands of kilometres. Two or more clans typically use an area, but the whales only socialize with members of their own clan. In many respects the closest parallel may be the ethno-linguistic groups of humans. Patterns and processes of human prehistory that may be instructive in studying sperm whale clans include: the extreme variability of human societies; no clear link between modes of resource acquisition and social structure; that patterns of vocalizations may not map well onto other behavioural distinctions; and that interacting societies may deliberately distinguish their behaviour (schismogenesis). Conversely, while the two species and their societies are very different, the existence of very large-scale social structures in both sperm whales and humans supports some primary drivers of the phenomenon that are common to both species (such as cognition, cooperation, culture and mobility) and contraindicates others (e.g. tool-making and syntactic language).
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".