Variation between ranch blue fox populations in cranial form
Bibliographic record
Abstract
The aim of this study was to describe the differences in cranial size and shape that occur between different farmed blue fox populations within and between countries. The skulls were obtained at the pelting time from three Finnish, one Estonian, one Norwegian and seven Canadian farms. The material was subjected to a principal component (PC) analysis for each sex. The first PC‐factor explained about 50% of variation. It was identified as the size factor; it discriminated Finnish blue foxes at one end and Nova Scotia foxes at the other end of the scale. The second PC‐factor explained about 10% of variation in skull morphology but failed to discriminate the populations. The third factor, which was dominated by interorbital width, discriminated Nova Scotia foxes from the other populations. PC‐factor 4 received its highest loading from the length of upper tooth row. This factor differentiated, although poorly, the Newfoundland fox populations, whether local or crosses between the local and the imported Finnish stock, from the others. The sexes were significantly different on most single parameters and the skulls of all farm populations were larger than those of wild Arctic foxes. However, there were also large differences in skull morphology between farms within one country. This shows that farmed blue foxes in different countries have not yet diverged into anatomically distinct populations.
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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 | 0.001 | 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".