Effects of micronization, tempering, and flaking on the chemical characteristics of wheat and its feeding value for broiler chicks
Bibliographic record
Abstract
Five experiments were conducted to study the effects of micronizing, tempering and flaking of wheat (cv. Genesis) on the chemical characteristics and its feeding value for broiler chicks. Samples of wheat were micronized (infrared invisible band with wavelengths of 1.8 to 3.4 µ) at 90, 105, and 120°C for 50 s, with or without tempering to 20% moisture, and with or without flaking. Micronization at 120°C produced negative effects on weight gain and feed efficiency (0–18 d). Tempering reduced protein solubility in 0.2% KOH, increased the solubility of dietary fibre, and increased in vivo digesta viscosity and in vitro extract viscosity as well as diet AME. Flaking wheat had no effect on broiler performance in exps. 3 and 4, but decreased growth rate and feed efficiency in exp. 5 despite improved dietary AME. Flaking decreased protein solubility at 120°C but had little effect at lower temperatures. The lowest protein solubility was found in the sample that was tempered, micronized at 120°C, and then flaked. In conclusion, micronization at 90 and 105°C improved the feeding value of wheat for broiler chickens. Tempering and flaking may be beneficial at lower micronization temperatures, but can result in lower nutritional value at higher micronization temperature. Key words: Wheat, micronization, metabolizable energy, broiler
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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) | 0.001 | 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 | 0.000 | 0.001 |
| 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".