Effect of rearing density on the growth and welfare indices of juvenile spotted wolffish,<i>Anarhichas minor</i>(Olafsen)
Bibliographic record
Abstract
The goal of this study was to determine the optimal stocking density for rearing juvenile spotted wolffish, Anarhichas minor (Olafsen), at two different sizes and assess the welfare status in relation to density. No major growth impairment was observed, although smaller fish (50–100 g) were significantly affected by density during the 120 days of the experiment, with final mean weights of 119.6 ± 11.6, 118.0 ± 5.8 and 88.7 ± 0.6 g for initial rearing densities of 10, 20 and 40 kg m−2 respectively. No effect of rearing density was seen for larger fish (100–160 g) during the 90 days of the experiment, with final mean weights of 160.2 ± 5.9, 159.7 ± 3.7 and 163.7 ± 11.5 g at fixed rearing densities of 20, 30 and 40 kg m−2 respectively. Our results suggest that the optimal rearing density of juvenile spotted wolffish is below 40 kg m−2 for smaller size fish (∼50–100 g) and probably ≥40 kg m−2 for the larger fish (100–160 g). Furthermore, it appears that the range of rearing density used did not have a significant effect on a selection of stress indicators (Na+ and K+ concentration, haematocrit, hepatosomatic index, total amount of plasma proteins and liver and muscle water content) and immunity response (plasma lysozyme activity) of juvenile spotted wolffish, making it a very tolerant species to crowding.
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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".