Feathering in commercial poultry II. Factors influencing feather growth and feather loss
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In commercial production, there is often concern about the quantity and/or quality of feathering in both broilers and layers. For broilers, the concern is adequacy of protective feather cover, while in layers it is usually the necessary degree of feathering needed to optimise feed efficiency. Feather development is under the control of hormones such as thyroxine and oestrogen and indirectly by testosterone. Environmental or nutritional status that influences such hormonal output will indirectly affect feathering. In broilers, rate of feathering is influenced by genetics, since some 20 years ago there was a conscious decision to introduce slow (K) vs. fast (k) feathering as a means of sexing day-old chicks. With the relative “immaturity” of modern broilers, these genes influence feather cover well into the production cycle. In White Leghorn crosses, initial problems with apparent Leukosis susceptibility of the progeny of slow feathering dams had to be overcome by eradication of Leukosis before feather sexing could be generally introduced. Nutrition can influence rate of feathering as well as feather structure, colour and moulting. Amino acid balance and especially deficiencies of TSAA and branched chain amino acids will influence feathering in young birds. Deficiency of vitamins and certain trace minerals also induce characteristic feather abnormalities, as does the presence of dietary mycotoxins. A number of viruses, bacteria and mycoplasma can infect the feather follicle and so influence feather development. Feather pecking and feather licking are behavioural abnormalities, although these conditions can be induced by changes in environmental conditions or nutritional adequacy of the diet.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it