The value of bioelectrical impedance analysis vs. condition indices in predicting body fat stores in North American porcupines (Erethizon dorsatum)
Bibliographic record
Abstract
We developed a predictive model to estimate body fat stores in a population of North American porcupines, Erethizon dorsatum (L., 1758). We trapped porcupines in the autumn of 2004 and spring of 2005. After collecting morphometric measurements on each animal, we used a plethysmograph to perform bioelectrical impedance analysis (BIA). We euthanized the subjects, measured two components of body composition (body fat, body water) via direct chemical analysis, and calculated lean dry mass to compare with BIA data. With regression we found the best predictive models for total body water, total body fat, percent body fat, and lean dry mass. We also estimated body condition for each animal using six different condition indices and compared the ability of the condition indices and our regression model to predict total body fat. Our model for total body fat accounted for 84% of the variation in fat measured by direct chemical analysis, and our model for percent body fat accounted for 78% of the variation. Two condition indices were significantly related to total body fat in porcupines and explained 45%–49% of the variation in observed body fat. We recommend BIA as a useful technique for estimating body fat stores in field studies of free-ranging porcupines and suggest abandonment of the use of condition indices as analogues of body fat stores in animal studies unless the indices can first be validated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 teacher head, 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".