An analysis of canine hair re‐growth after clipping for a surgical procedure
Bibliographic record
Abstract
Hair growth and replacement have been studied extensively in humans, sheep and laboratory rodents, but in dogs and other mammalian species few studies have been published. The objectives of this study were: (1) to determine the time required for the hair to re-grow in dogs after clipping for a surgical procedure; (2) to define whether the season of the year influenced the period of time required for re-growth and; (3) to determine if season might influence the telogen: anagen ratio. Eleven Labrador retrievers were recruited during spring, 10 during summer, six during autumn and 10 during winter. Hairs re-grew to their preclipped length in 14.6 weeks, 14.5 weeks, 13.6 weeks and 15.4 weeks when shaved in the spring, summer, autumn and winter, respectively. The differences in these values were not significant suggesting that season has no effect on the rate of hair re-growth in Labrador retrievers housed indoors (P = 0.12). The mean values for the telogen: anagen ratio in each season were: 5.2 (spring), 6.1 (summer), 9.5 (autumn), and 5.3 (winter). The differences in these values also were not significant (P = 0.89). The percentage of hairs in telogen was over 80% in all four seasons.
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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".