The Cricket (Gryllus assimilis) as an Alternative Food Versus Commercial Concentrate for Tilapia (Oreochromis sp.) in the Nursery Stage
Bibliographic record
Abstract
In this paper, a 32% commercial diet of protein for tilapia fingerlings (Oreochromis sp.) commonly used in fish farms in the country and cricket meal in a mixture with 32% corn flour as a protein was compared alternative food The breeding, raising and fattening unit of crickets (Gryllus assimilis) was implemented for its subsequent sacrifice and transformation into flour and used as a source of protein in the diet, these gained a weight of 0.70 g. To accommodate the fry, plastic tanks with a capacity of 1 m3 were used, the water was sucked by means of an electric pump; with permanent aeration where 15 individuals of red tilapia per m3 were planted with an average weight of 5 g which reached an average weight/fish/day of 26 g, a growth/fish/day of 0.86 g, the total biomass was 340 g, and a feed conversion factor (FCA) of 1.16. According to the Student’s T analysis and a comparison of means, no differences were found, with a correlation of 83.1% between the food supplied and the increase in weight, the water parameters were found at an optimum level. The protein percentage of cricket flour ranges between 15% and 80% depending on the geographical area, those that were used in the local ration have 58.16% of crude protein and 9.32 of protein nitrogen, the two rations in comparison had a protein content of 32%.
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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) | 0.002 | 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".