EQUAÇÕES DE PREDIÇÃO PARA VALORES DE PROTEÍNA E ENERGIA DIGESTÍVEIS EM ALIMENTOS DE ORIGEM ANIMAL PARA TILÁPIAS
Bibliographic record
Abstract
The objective of this study was to formulate mathematical models to estimate values of digestible protein and energy of feeds for tilapias. Papers containing data on chemical composition of crude protein, ether extract and mineral matter, in addition to values of digestible protein and energy obtained in biological assays were used. The data were subjected to multiple linear regression, stepwise backward. Additionally, a digestibility trial with juvenile Nile tilapias of the GIFT strain was conducted to test five meat and bone meals to validate the obtained models and elaborate individual models for the ingredients. Values of digestible protein and energy of meat and bone meals were obtained using Guelph system to feces collection and chromium (III) oxide was used as indicator. It was not possible to obtain a reliable model to estimate digestible energy (DE) values of the ingredients. It was concluded that the model to estimate digestible protein values (DP) of animal origin is: PD (%) = 0.970 x CP - 0.290 x MM; R2 = 0.998. The models to estimate the digestible protein and energy values of the meat and bone meal were: DP (%) = 3.460 x EE - 0.347 x MM; R2 = 0.998 and DE (kcal/kg) = 6700.119 - 101.368 x MM; R2 = 0.965, respectively.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".