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Record W4320480744 · doi:10.1016/j.animal.2023.100734

DM intake by feedlot beef cattle: factors that impact intake patterns

2023· article· en· W4320480744 on OpenAlexaff
Antônio M. Silvestre, G. D. Cruz, F. N. Owens, M. C. Pereira, R. B. Hicks, D. D. Millen

Bibliographic record

Venueanimal · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPharmacological Effects and Assays
Canadian institutionsUniversity of Saskatchewan
FundersUniversidade Estadual PaulistaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsFeedlotAnimal scienceBeef cattleMathematicsStatisticsBiology

Abstract

fetched live from OpenAlex

DM intake (DMI) for individual pens of cattle is recorded daily or averaged across each week by most commercial feedlots as an index of performance. Numerous factors impact DMI by feedlot cattle. Some are available at the start of the feedlot period (initial BW, sex), and others become available early in the feeding period (daily DMI during adaptation) or more continuously (daily DMI from the previous week). To evaluate the relative impact of these factors on daily DMI during individual weeks within the feedlot period, we employed a dataset compiled from 2009 to 2014 from one commercial feedlot, including 4 132 pens (485 458 cattle), which were split into two fractions: 80% were used to calculate DMI regressions on these factors to develop a prediction equation for mean DMI for each week of the feeding period, and 20% were reserved to test the adequacy of these prediction equations. Correlations were used to determine the relationship between all available variables with observed DMI. These variables were then included in the generalized least squares regression models. A veracity test of the model was performed against the reserved data. Daily DMI from previous week was the factor most highly correlated with daily DMI (P < 0.10) during from week 6 to week 31, accounting for approximately 70% of the variation, followed by mean daily DMI during adaptation period (weeks 1–4), including in the prediction model from weeks 5 to 12. Initial shrunk BW (ISBW) was the third most correlated factor, which was included in prediction equations from week 5 to week 20. Sex entered the prediction model only after week 8. Daily DMI for each test week within the feeding period was predicted closely (r2 = 0.98) by these four factors (RMSE = 0.155 kg). In conclusion, the mean daily DMI during each week of the finishing period for a pen of cattle could be predicted closely based on mean daily DMI intake during the previous week plus other variables available early in a feedlot period (daily DMI during adaptation period, ISBW and sex).

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.287
Teacher spread0.243 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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