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Record W4392810464 · doi:10.1080/00439339.2024.2323536

Review: recent advances and future technologies in poultry feed manufacturing

2024· article· en· W4392810464 on OpenAlex
Jihao You, J.L. Ellis, Dan Tulpan, Mark C. Malpass

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWorld s Poultry Science Journal · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsPoultry farmingBusinessManufacturing engineeringEngineeringBiologyEcology

Abstract

fetched live from OpenAlex

Poultry feed manufacturing refers to processing various raw materials to meet birds' nutrient requirements, based on knowledge of animal nutrition and mechanical engineering. Since the advent of feed mills, many technologies have been utilised to implement various feed manufacturing techniques, which are helpful to sustainably produce well-balanced, cost-effective and high-quality feed. Efforts have been made to further strengthen the environmental, social and economic sustainability of feed manufacturing via a variety of technological innovations over the years. The integration and application of new technologies will be helpful to further improve poultry feed manufacturing in the future as well. By increasing the precision and uniformity of the final feed, and reducing the risk of errors, these advancements will ultimately result in more efficient manufacturing of animal products, optimising both the nutrition supplied and the energy used. This paper will review recent advances in the feed industry for poultry, as well as envision new technologies.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.282
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it