A body-condition index for ursids
Bibliographic record
Abstract
In this investigation a body-condition index (BCI) was developed for polar bears (Ursus maritimus), black bears (Ursus americanus), and grizzly bears (Ursus arctos), based on residuals from the regression of total body mass against a linear measure of size, straight-line body length (SLBL). Transformation of masslength data from 1198 polar bears, 595 black bears, and 126 grizzly bears to natural logarithms resulted in a linear relationship between mass and length. However, the relationship in polar bears differed from that in black and grizzly bears. SLBL had a close positive relationship with skeletal (bone) mass in polar bears (n = 31) and black bears (n = 33), validating the use of SLBL as an accurate index of body size. There was no correlation between SLBL and BCI for polar bears (r = 0.005, p = 0.87, n = 1198) or for black bears and grizzly bears (r = 0.04, p = 0.30, n = 721), indicating that the BCI was independent of body size. The BCI had a close positive relationship with true body condition, measured as the standardized residual of the combined mass of fat and skeletal muscle against SLBL, in polar and black bears that were dissected to determine individual tissue masses. The BCI also had a close positive relationship with the standardized residual of fat mass against SLBL. Estimation of BCI values for polar bears, or for black bears and grizzly bears, is facilitated by prediction equations that require measurement of total body mass and SLBL for individual animals.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".