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Record W4402541348 · doi:10.1093/jas/skae234.903

PSIX-28 Optimization of feeding trial length for evaluating feed efficiency in sheep

2024· article· en· W4402541348 on OpenAlexaff
Yaogeng Lei, Olufemi Osonowo, Désirée Gellatly, Hamza Jawad, Sean Thompson, Ghader Manafiazar

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsDalhousie UniversityOlds College
Fundersnot available
KeywordsAnimal scienceFeed conversion ratioBiologyBody weightEndocrinology

Abstract

fetched live from OpenAlex

Abstract Feed efficiency tests are well established for beef cattle, but testing for sheep remains rare due to the lack of appropriate equipment and standardized protocols. To explore the optimum trial length for evaluating feed efficiency in sheep, freshly weaned Rideau Arcott ewe lambs [n = 80; 4to 5 mo of age and body weights (BW) of 16 to 25 kg] were enrolled in a 65-d feeding trial, following a 20-d adaptation period. A mixed ration containing barley gain and protein supplement pellets were offered twice daily, and lambs had ad libitum access to feed, water and bedding straw during the trial period. Daily feed intake of individual lambs was monitored with Vytelle SENSE feed bunks, BW was recorded biweekly, as well as on two consecutive days at the start and end of the trial. Lambs that had died or had abnormal growth patterns from sickness were removed, resulting in 62 lambs remaining in the final dataset. Optimum trial length for measuring feed intake (DMI) and average daily gain (ADG) were evaluated with R Software (v. 4.3.3) via a post-hoc analysis of constructing shortened trial lengths from the regular test. The DMI and ADG values from the shortened trial datasets were compared with those from regular trial days with correlation (Pearson and Spearman tests) and linear regression analyses. There were 64 shortened trial lengths for DMI by 1 d increments, and 15 sub-datasets for ADG consisting of trial lengths between every two BW measurements. Residual feed intake (RFI), residual average daily gain (RADG), and residual intake and BW gain (RIG) were calculated for and compared between the regular trial length (65 d) and favorable shortened trial lengths based on DMI and ADG evaluations. Correlation, regression and agreement analyses were used to determine the optimum trial lengths for RFI, RADG and RIG evaluations. Results showed that DMI and ADG measurements could be shortened to 42 (r > 0.95, R2 > 0.90, β > 0.95, P < 0.001) and 56 d (r > 0.96, R2 > 0.93, β > 0.96, P < 0.001), respectively. Additionally, trial length for RFI, RADG, and RG evaluations could be reduced to 56 d (r > 0.95, R2 > 0.91, β > 0.95, CCC > 0.95, P < 0.001), primarily due to the trial length requirement for accurate ADG measurement. Reducing the duration of trials for evaluating feed efficiency in sheep not only reduces testing costs, fostering greater uptake among sheep producers, but also allows for the evaluation of more lambs annually, thereby enhancing the overall productivity and profitability of the sheep industry.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.037
GPT teacher head0.307
Teacher spread0.271 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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