Development of Computer Models for Fish Feeding Standards and Aquaculture Waste Estimations: A Treatise
Bibliographic record
Abstract
Feeding guides for salmonids have been available from various sources for many years. These guides have originated inone way or another from earlier feeding charts of 1950-60s when meal-meat mixture diets were widely used. Few of thefeeding guides available today are based on actual bioenergetic data at different water temperatures and are adapted tohigh energy diets.New feeding standards have been developed by Cho et al. (1976, 1980, 1982, 1990 and 1992) and these are based onprinciple of nutritional energetics in which the digestible energy content of diet, digestible protein and energy ratio, andthe amount of digestible energy required to produce per unit of live weight gain. The gain expressed as retained energyin carcass and maintenance energy at different water temperatures is the main criteria for daily energy and feedallocations.Using past production records as a starting point, ration allowance and waste outputs can scientifically be tabulatedbased on the following concepts: Prediction of growth and nutrient/energy gains, estimation of faecal andmetabolic waste outputs and allocation of energy and nutrient needs.Series of bioenergetic models were developed and a stand-alone multimedia computer program (Fish-PrFEQ) for theWindows™ platform was written in MS Visual C++.NET language with database functionality. This program predictsenergy, nitrogen and phosphorus retention and excretions to determine growth, feeding standards, waste output andeffluent water quality.The Fish-PrFEQ program also contains modules for production records and data base management for input and outputdata which may be exported for further data and graphic manipulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".