Bibliographic record
Abstract
The need to precisely measure growth is a common denominator in many fisheries studies, but growth measures other than total masses or lengths are nearly nonexistent because more precise measurements such as body composition analysis are often too difficult and time consuming. Here, we present a means of estimating body composition in fish quickly, and after validation, without the need to sacrifice the animal. Models built with brook trout (Salvelinus fontinalis) were linear with strong validation group relationships (R2 > 0.96) for composition parameters including water, protein, fat, fat-free, and dry masses. Subject responses to bioelectrical impedance analysis were minimal, with only slight bruising (p < 0.001) with no effect on swimming, color, bleeding, or feeding. The model was also tested on the water and dry masses of 10 warmwater fish species and found to have strong correlations (R2 > 0.86), suggesting that more general relationships may exist. Nonlethal estimation of body composition using bioelectrical impedance analysis will permit increased precision in bioenergetics energy flow and compositional studies as well as permit study of community energetics and condition on spatial and temporal scales not previously possible.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".