Evaluating indices of nutritional condition for caribou (<i>Rangifer tarandus</i>): which are the most valuable and why?
Bibliographic record
Abstract
Body composition studies are critical for evaluating the accuracy of nutritional condition indices for predicting body components. We evaluated >40 indices of nutritional condition for caribou (Rangifer tarandus (Linnaeus, 1758)) using 29 female caribou captured from three populations in Alaska (USA) that ranged in condition from 2.3% to 11.2% ingesta-free body fat (IFBF) and 6 captive female caribou that ranged in condition from 8.1% to 26.0% IFBF. Estimates of body fat, protein, and gross energy were regressed against each index of nutritional condition. Generally, indices with linear or slightly curvilinear relations to body fat and those based on multiple fat depots were the most accurate in predicting nutritional condition and the most useful over the full range of nutritional condition. A scaledLIVINDEX (a combination of subcutaneous fat thickness and a condition score), CONINDEX (a combination of kidney fat and marrow fat), and a subset of the Kistner score (pericardium and kidneys only) had the strongest relationship with body fat (r2 > 0.86) and were useful over the entire range of nutritional condition. If used properly and with adequate training, indices of nutritional condition can be a critical tool for understanding the severity and seasonality of nutritional limitations in wild caribou 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".