Alternative Methods for Measuring Energy Content in Winter Flounder
Bibliographic record
Abstract
Abstract Indices of energy reserves may represent interesting parameters that can be used as bioindicators in environmental studies. The goal of this study was to identify a water–energy model that could predict energy reserves in winter flounder Pseudopleuronectes americanus. Winter flounder kept in captivity and fed different food types (either capelin Mallotus villosus or Atlantic herring Clupea harengus, amphipods Anonyx sarsi, and wet pellets) for 2, 5, and 14 months and wild fish captured in May, July, and October were used to show a large range in energy content. High levels of correlation were observed between water and energy contents in fish carcasses (r2 = 0.82) and muscle (r2 = 0.75). However, the biochemical composition of the liver remained relatively constant, despite changes in the hepatosomatic index. The condition factor (somatic weight/length3) was associated with energy reserves (i.e., water contents), but the coefficients of determination were smaller (0.18 < r2 < 0.34). We found that muscle water content, which can easily be determined, is an efficient way to accurately predict energy reserves in winter flounder.
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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) | 0.001 | 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".