Mating ecology of beluga (<i>Delphinapterus leucas</i>) and narwhal (<i>Monodon monoceros)</i>as estimated by reproductive tract metrics
Bibliographic record
Abstract
Abstract Narwhal and beluga whales are important species to Arctic ecosystems, including subsistence hunting by Inuit, and little is understood about their mating ecology. Reproductive tract metrics vary across species in relation to mating strategy, and have been used to infer mating ecology. Reproductive tracts from beluga and narwhal were collected between 1997 and 2008 from five beluga stocks and two narwhal stocks across the Canadian Arctic. Tract length for males and females, relative testes mass for males, and tusk length for male narwhal were measured. We assessed variation relative to species, body size, stock, maturity, and season. Significant variation was found in testes mass across month and stock for beluga, and no significant difference between stock or date of harvest for narwhal. Beluga had significantly larger testes relative to body size than narwhal, suggesting they were more promiscuous than narwhal. A significant relationship was found between narwhal tusk length and testes mass, indicating the tusk may be important in female mate choice. No significant differences were found between narwhal and beluga reproductive tract length for males or females. The mating systems suggested for narwhal and belugas by our results mean the two species may respond differently to climate change.
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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 | 0.001 | 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".