Measuring fish condition: an evaluation of new and old metrics for three species with contrasting life histories
Bibliographic record
Abstract
Measuring fish condition should link ecosystem drivers with population dynamics, if the underlying physiological basis for variations in condition indices are understood. We evaluated traditional (K, Kn, hepatosomatic index, gonadosomatic index, energy density, and percent dry weight of muscle (%DWM) and liver (%DWL)) and newer (bioelectrical impedance analysis (BIA) and scaled mass index (SMI)) condition indices to track seasonal cycles in three flatfishes — winter founder (Pseudopleuronectes americanus; three stocks), yellowtail flounder (Limanda ferruginea; three stocks), and summer flounder (Paralichthys dentatus; one stock) — with contrasting life histories in habitat, feeding, and reproduction. The %DWM and %DWL were good proxies for energy density (r2 > 0.96) and more strongly related to K, Kn, and SMI than to BIA metrics. Principal component analysis indicated many metrics performed similarly across species; some were confounded by size, sex, and maturity along PC1, while others effectively characterized condition along PC2. Stock differences were along PC1 in winter flounder, reflecting different sizes across stocks, whereas in yellowtail flounder differences occurred along PC2 related to condition. These comparisons, within and across species, highlight the broad applicability of some metrics and limitations in others.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".