Dietary protein requirements of juvenile haddock (<i>Melanogrammus aeglefinus</i> L.)
Bibliographic record
Abstract
A study was conducted to determine growth and feed utilization by haddock fed diets containing graded levels of protein (35, 40, 45 and 50%). Haddock fingerlings with an average weight of 24 g were hand-fed one of the four isoenergetic (≈16.6 MJ digestible energy kg−1) experimental diets to satiation, three times a day during the 9-week period. Filtered and UV-treated water (salinity, 30‰) was supplied to each circular tank (holding capacity: 320 L) at 4 L min−1 in a flow-through system. Increases in dietary protein improved weight gain, specific growth rate (SGR) and feed : gain ratio. The highest weight gain (percentage/initial weight) was observed in fish fed 50% protein, although there was no significant difference between groups fed 45% and 50% protein. A similar effect was observed in SGR of fish fed 50% protein, which was the highest among treatments. Although an increase in dietary protein resulted in a slight increase in feed intake, the lowest feed : gain ratio was obtained in fish fed the diet with the highest protein. Nitrogen intake increased from 1.48 to 2.33 g with the increase in dietary protein levels, which resulted in an improvement in whole-body nitrogen gain, although there were no significant differences in nitrogen retention and protein efficiency ratio among fish groups. The broken-line regression of weight gain against protein level yielded an estimated protein requirement of 49.9%.
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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 | 0.000 |
| 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".