Growth and feed utilization of large size rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) reared in freshwater: diet and species effects, and responses over time
Bibliographic record
Abstract
Four diets differing in crude protein/crude lipid concentrations (CP/CL), 570/200, 510/220, 460/240, 430/260 (g kg−1 dry diet) were fed to near-satiety to rainbow trout (initial body weight, IBW = 268 g, domestic strain) and Atlantic salmon (IBW = 456 g, wild strain) for 308 days to determine the effect of diets, species and fish size on efficiency of feed, nitrogen (N) and energy utilization. Within species, weight gain, feed efficiency (FE), and energy retention efficiency (ERE, E gain/E intake) were not affected by diet (P < 0.05). N retention efficiency (NRE, N gain/N intake) increased linearly (P < 0.05) with decreasing CP/CL for both species. NRE and FE were higher for salmon compared with trout. Whole body CP of salmon was significantly (P < 0.05) higher, and lipid significantly (P < 0.05) lower, than those of trout. There was a significant (P < 0.05) linear decrease in FE as fish grew, regardless of diet and species. NRE linearly decreased (P < 0.0001) and lipid to protein deposition ratio (LD/PD) increased (P < 0.05) as trout grew. NRE linearly increased (P < 0.05) while ERE and LD/PD linearly decreased (P < 0.05) as salmon grew. Salmon appeared to rely to a greater extent on non-protein energy-yielding nutrients to support cost of growth than did trout.
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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".