The potential of land animal protein ingredients to replace fish meal in diets for cuneate drum,<i>Nibea miichthioides</i>, is affected by dietary protein level
Bibliographic record
Abstract
A net pen experiment was carried out to examine the effect of dietary protein level on the potential of land animal protein ingredients as fish meal substitutes in practical diets for cuneate drum Nibea miichthioides. Two isocaloric basal (control) diets were formulated to contain 400 g kg−1 herring meal but two different digestible protein (DP) levels (400 versus 350 g kg−1). At each DP level, dietary fish meal level was reduced from 400 to 280, 200, 80 and 0 g kg−1 by incorporating a blend that comprised of 600 g kg−1 poultry by-products meal (PBM), 200 g kg−1 meat and bone meal (MBM), 100 g kg−1 feather meal (FEM) and 100 g kg−1 blood meal (BLM). Cuneate drum fingerling (initial weight 42 g fish−1) were fed the test diets for 8 weeks. Fish fed the test diets exhibited similar feed intake. Final body weight, feed conversion ratio and nitrogen retention efficiency was not significantly different between fish fed the basal diets containing 350 and 400 g kg−1 DP. Weight gain decreased linearly with the reduction of dietary fish meal level at the 350 g kg−1 DP level, but did not decrease with the reduction of dietary fish meal level at the 400 g kg−1 DP level. Results of the present study suggest that fish meal in cuneate drum diets can be completely replaced with the blend of PBM, MBM, FEM and BLM at the 400 g kg−1 DP level, based on a mechanism that excessive dietary protein compensate lower contents of bio-available essential amino acid in the land animal protein ingredients relative to fish meal.
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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".