ORIGINAL PAPER Geographic variation in cranial morphology of narwhals (Monodon monoceros) from Greenland and the eastern Canadian Arctic
Bibliographic record
Abstract
Abstract We analyzed variation in nine non-metric and eight metric variables in the skulls of 132 narwhals (Monodon monoceros) from five localities in Greenland (Inglefield Bredning, Melville Bay, Uummannaq, Disko Bay, and Scoresby Sound) and one in the eastern Canadian Arctic (Eclipse Sound). Metric variables were used to compare the combined Disko Bay and Uummannaq sam-ples with the samples from Inglefield Bredning and Scoresby Sound using three different multivariate tech-niques for each sex. None of the results were significant. Seven of the non-metric variables were independent of age and sex and were used in comparing samples from the six localities. No differences were found among the four localities in West Greenland, but differences were found in two of the non-metric variables between the combined West Greenland sample and the one from Scoresby Sound. A major shortcoming of the analysis based on metric data was the small sample size from several of the areas, which resulted in low statistical power. Genetic as well as envi-ronmental factors could explain the differences detected here between narwhals living along the west and the east coasts of Greenland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".