Body condition in Svalbard reindeer and the use of blood parameters as indicators of condition and fitness
Bibliographic record
Abstract
Body condition is an important determinant of ecological fitness but is difficult to measure in field studies of live animals. Live mass and subcutaneous fat are often used as proxies for body condition and related to fitness. We investigated the relationship between blood-chemistry parameters and live mass and back-fat thickness and assessed their usefulness as predictors of ecological fitness in a wild arctic ungulate population, Svalbard reindeer (Rangifer tarandus platyrhynchus). Female reindeer were sampled in late winter between 1995 and 2002 and concentrations of blood parameters were related to subsequent survival and successful calving. There was marked annual variation in all blood parameters, live mass, and back-fat thickness, reflecting variation in weather and food availability. At the individual level, variation in blood-parameter concentrations was not closely related to variation in live mass or back-fat thickness, instead reflecting shorter term nutritional status. Blood parameters could therefore provide useful additional information, enhancing the predictive power of fitness models based on live mass. The urea:creatinine ratio significantly improved adult survival models, while β-hydroxybutyric acid and creatinine concentrations were significant predictors of calving success. The applications for blood parameters in ecological investigations look promising and should be tested more widely in other field studies.
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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 | 0.000 | 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) | 0.000 | 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".