Broiler responses to digestible total sulphur amino acids at different ages: a neural network approach
Bibliographic record
Abstract
Three experiments were conducted with broiler chickens to evaluate the effects of digestible total sulphur amino acid (TSAA) on their performance at three different phases of starter (1–14 d), grower (15–28 d) and finisher (29–42 d). The measured traits included: average daily gain (ADG), feed intake, feed conversion ratio (FCR), carcass protein, body lipid (BL), feather weight gain, carcass plus feather protein, carcass TSAA deposition and nitrogen excretion (NE). A dilution technique was used to create seven diets (with eight replicates) increasing the TSAA content from 2.5 to 9.04 g/kg of diet for starter, 2.26 to 8.14 g/kg of diet for grower and 2.08 to 7.5 g/kg of diet for finisher. Data measured were imported to neural networks to predict the measured traits in response to dietary and intake levels of TSAA and find the optimal levels of TSAA that lead to the desired responses. Optimization results showed decreases in optimal dietary TSAA values with increasing age for all traits, while reverse was observed for intake values and requirements were increased as birds aged. The highest TSAA requirement (7.95, 7.2 and 6.6 g/kg and 283, 585 and 1150 mg/bird per d for starter, grower and finisher, respectively) were achieved for minimum BL and lowest (5.8, 5.2 and 4.9 g/kg and 201, 444 and 873 mg/bird per d for starter, grower and finisher, respectively) were suggested for minimum NE. Based on intake models, the optimal TSAA values for minimum FCR in phases 1–3 were 283, 585 and 1150 mg/bird per d while maximum ADGs were achieved with 201, 444 and 873 mg/bird per d of TSAA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".