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Record W1990551610 · doi:10.2460/javma.2000.216.1802

Lifetime reproductive and financial performance of female swine

2000· article· en· W1990551610 on OpenAlexaff
Thomaz Lucia, Gary D. Dial, William E. Marsh

Bibliographic record

VenueJournal of the American Veterinary Medical Association · 2000
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsMinistry of Energy, Northern Development and Mines
Fundersnot available
KeywordsHerdLitterBiologyAnimal sciencePopulationReproductionWeaningDemographyAgronomyEcology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate reproductive and financial performance for commercial swine herds grouped on the basis of pattern of removal of female swine. DESIGN: Cohort study. SAMPLE POPULATION: 25 swine herds. PROCEDURES: Lifetime reproductive productivity was summarized as number of pigs weaned per herd day per mated female and as number of herd days per pig weaned per mated female. Factors associated with these 2 measures were determined by use of linear regression. Financial data from a commercial database were used to estimate maximum number of parities at removal associated with profitability. Sensitivity analysis was used to simulate how variations in daily maintenance cost and value per weaned pig would influence profitability. RESULTS: Mean number of pigs weaned per herd day per mated female was 0.054; mean number of herd days per pig weaned per mated female was 20.2. Both these measures were associated with proportion of nonproductive days during herd life, preweaning mortality rate per litter weaned, mean lifetime number of pigs born alive per litter weaned, and mean lifetime lactation duration. Maximum parity at time of removal associated with profitability ranged from 5 to 8. Daily maintenance costs per female had a greater impact on lifetime profitability than did value per weaned pig. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggest that lifetime reproductive and financial performance is optimized among swine herds that have higher proportions of high-parity females.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.258
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.025
GPT teacher head0.311
Teacher spread0.286 · 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

Citations42
Published2000
Admission routes1
Has abstractyes

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