The role of the crop in poultry production
Bibliographic record
Abstract
The importance of the crop is often underestimated in poultry production. In addition to storing ingested feed, it also can impact nutrient digestion by digesta softening and the initial activity of feed (endogenous and exogenous) and microbial enzymes. The crop represents the first major defence against poultry pathogens and zoonotic organisms with well established adaptive and innate immune function, and a lactobacilli dominated microbiota capable of reducing the passage of these organisms further along the digestive tract. However, the potential to improve bird productivity and health, as well as affect meat and egg safety, are influenced by the nature of the diet, and in particular feed entry and extended presence in the crop. This is required to promote lactobacilli fermentation, the production of lactic acid and other volatile fatty acids, and the lowering of crop pH. Management practices such as meal feeding and the use of lighting programs with extended dark periods encourage crop utilisation. Further, the use of feed additives such as prebiotics and probiotics may enhance crop function, which in turn contributes to well-being of the entire digestive tract. A healthy and functional crop, along with other segments of digestive tract, has increased importance in an era of reduced antibiotic use in poultry feeds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".