Modeling of Parameters Affecting Phytate Phosphorus Bioavailability in Growing Birds
Bibliographic record
Abstract
The current study was undertaken to establish a population from an unselected random-mating chicken population for the development of a model to predict factors that affect phytate P utilization in growing birds. A population was established from a mating of 40 male and 200 female chickens from the Athens Canadian randombred population. At 4 wk of age, birds were housed in individual metabolic cages and fed a diet containing 1.06% Ca, 0.35% total P, and 0.03% available P. After 3 d of acclimatization, feed consumption (FC) was measured and excreta produced in 3 consecutive d were collected. Individual 4-wk BW, BW gain (BWG), phytate P intake (PPI), inorganic P intake (IPI), Ca intake (CaI), N intake (NI), and energy intake (EI) during the 3 d excreta collection period were also measured. Feed conversion ratios (FCR) and relative growth rate were calculated. Phytate P bioavailability (PPB), Ca bioavailability (CaB), and N bioavailability (NB) were estimated from the disappearance of the nutrients during the passage of feed through the gastrointestinal tract. Energy bioavailability (EB) was measured by bomb calorimetry as the difference in the gross energy of the feed and the energy of the excreta. The major factors affecting PPB were CaB and EB for both sexes. In the males, BW contributed significantly to PPB. However, in the females, NB also contributed significantly to PPB. Faster growing birds tended to have a reduced retention time of feed compared with slow growing birds, and as a result utilized phytate P less. Birds that are able to utilize phytate P better are putatively able to release P for energy utilization. Therefore, the birds that were able to utilize phytate P better were also better energy utilizers.
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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) | 0.000 | 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 | 0.001 | 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".