Empirical relationships between body tissue composition and bioelectrical impedance of brook trout <i>Salvelinus fontinalis</i> from a Rocky Mountain Stream
Bibliographic record
Abstract
Bioelectrical impedance analysis (BIA) analysis was carried out in the field on anaesthetized Salvelinus fontinalis electrofished from a mountain stream in Alberta, Canada; the fish were then sacrificed for subsequent analysis of tissue composition. Water content was assessed by comparing wet and dry mass, and total body lipid content was measured by Soxhlet extraction with petroleum ether. A multivariate analysis of body composition and size metric against impedance measurements was carried out, and the main findings were (1) body size and related metrics were strongly related to volumetric impedance measures, as shown in several previous studies, (2) lipid content (%) and water content (%) were both well predicted by regression models whose main predictor was reactance and (3) reactance and resistance measures that were series-based produced excellent predictions of tissue composition, whereas the corresponding parallel-based models were crude. The BIA measurements are quick and easy to conduct and appear to provide excellent predictions of a number of proximate body components, without the need to kill the fish; however, more studies are required to provide improved understanding of possible effects of region, season, life stage and species on measurements.
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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 | 0.000 | 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".