Lifetime patterns in adult female mass, reproduction, and offspring mass in semidomestic reindeer (<i>Rangifer tarandus tarandus</i>)
Bibliographic record
Abstract
The objective of our investigation was to examine relationships between lifetime patterns of female mass, calving incidence, and offspring mass in semidomestic reindeer (Rangifer tarandus tarandus). The analyses were made on data recorded from 1986 to 1997 on semidomestic reindeer in the herding district of Ruvhten Sijte in Sweden. A female rearing a calf was shown to weigh 3.1 ± 0.3 kg (mean ± SE) less in winter than a female not rearing a calf. The difference in calving incidence between females rearing a calf in the previous year and those not rearing a calf in the previous year was small, but there was a tendency for old females to have a decreased calving incidence if they had reared a calf in the previous year. The average calving percentage for adult females was 73%. Calf masses were affected by both the mother's mass and her age. The regression coefficient of calf autumn mass on female mass the previous winter was 0.26 ± 0.02. Mothers aged 24 years had calves that weighed less than calves of older mothers. The average difference in autumn mass between male and female calves was 2.9 ± 0.2 kg, but the difference was less for calves with mothers aged 24 years. We obtained lifetime patterns of female mass, calving incidence, and offspring mass. We also gave an organized structure of relationships between these traits.
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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".