Pattern and cost of growth and nutrient deposition in fish and shrimp: Potential implications and applications
Bibliographic record
Abstract
Growth is a factor of prime importance in aquaculture. A better understanding of growthcould result in significant benefits in terms of productivity, sustainability and profitability for aquacultureoperations, provided this greater understanding is translated into relevant and simple applications. The useof growth models, for example, offers an objective and practical way of describing pattern of growth andpredicting production. Growth involves the accretion of body components. The amounts of bodycomponents deposited and the cost of depositing these components are the main factors determining feedrequirement and waste outputs of fish, shrimp and other aquatic animals. This paper examines growth andnutrients deposition and utilization of fish and, to some extent, shrimp under aquaculture conditions.Simple approaches or models for describing and predicting growth, body composition, and feed requirementof fish and shrimp under aquaculture conditions are presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".