CONDITION INDICES AND BIOELECTRICAL IMPEDANCE ANALYSIS TO PREDICT BODY CONDITION OF SMALL CARNIVORES
Bibliographic record
Abstract
Body condition directly affects survival and reproduction by animals, so its effects on fitness represent an important component of animal ecology. Traditionally, ecologists have relied on direct chemical analysis or morphometric indices to assess body condition. We examined the ability of morphometric indices and bioelectrical impedance analysis to estimate body condition of raccoons (Procyon lotor) and assessed the need for species-specific models. Morphological indices were poor estimators of body condition; the best model explained 62% of the variation of fat and had a high SE (r2 = 0.62, SE = 0.52, P < 0.001). Bioelectrical impedance analysis proved to be a reliable way to noninvasively estimate body condition. Models for lean dry mass and total body water were used to accurately estimate body fat (r2 = 0.94, SE = 0.16, P < 0.001). Body fat estimates derived through models for a similar species performed better than morphometric indices but did not achieve the accuracy of the species-specific model. Examination of our data highlights the need to validate models used to estimate body condition before use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".