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Record W4321510040 · doi:10.21423/aabppro20104100

Disposition

2010· article· en· W4321510040 on OpenAlexaboutno aff
Darrell Busby

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsnot available
Fundersnot available
KeywordsDispositionFeedlotAnimal scienceTemperamentBiologyPsychologyPersonalitySocial psychology

Abstract

fetched live from OpenAlex

Disposition or temperament of cattle is a measure of the animal's relative docility, wildness, and handling ability during processing in the pen as well as in the handling facilities. Easily excitable animals compromise both their own safety and the safety of handlers. The Iowa Tri-County Steer Carcass Futurity collects sires, dams, and birth dates from cow-calf producers who retain ownership, as well as growth data, health treatments, disposition scores, and complete carcass data on steers and heifers. In the last 10 years data has been collected on 66,620 head of cattle from 23 states and Manitoba. Cattle are disposition scored at on-test, reimplant, and first sort; the cattle in the second harvest group are scored one additional time. Based on their average disposition score, the cattle were grouped as docile, restless, and aggressive.
 When compared to docile cattle, aggressive cattle gained less in the feedlot (2.91 vs 3.17 lb/day; 1.32 vs 1.44 kg/day), produced fewer Choice carcasses (58.1 vs 72.4%), more Select carcasses (36.2 vs 23.3%), and the black-hided cattle produced a lower percentage of Certified Angus Beef (CAB) carcasses (14.3 vs 29.1 %). Morbidity rates were similar across disposition scores; however, death loss increased significantly as disposition scores increased. Non-replacement heifers had higher disposition scores than steer mates, as cow-calf producers selected for more docile replacement heifers. Average profit for docile cattle was $46.63 per head compared to $7.62 per head for aggressive cattle.

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 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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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 teacher head, 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
Published2010
Admission routes1
Has abstractyes

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