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Record W2887680855 · doi:10.5539/sar.v7n4p11

Effects of Heifer Calving Date on Longevity and Lifetime Productivity in Western Canada

2018· article· en· W2887680855 on OpenAlexafffundvenueabout
Daalkhaijav Damiran, Kathy Larson, Leah Pearce, Nathan Erickson, H.A. Lardner

Bibliographic record

VenueSustainable Agriculture Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsIce calvingAnimal scienceHerdWeaningBiologyLongevityPeriod (music)LactationPregnancy

Abstract

fetched live from OpenAlex

The objective of this study is to determine the effect of calving early as a heifer on lifetime production in western Canada. This study evaluated the longevity and life time production data on 211 individual heifers (data gathered for 16 years) at the Western Beef Development Centre (WBDC), Saskatchewan. Heifers were classified as calving in the first (period 1; n= 87), second (period 2; n = 66), or third (period 3; n = 58) 21-day period of the calving season. For each subsequent calf born to the cow, calving period was reassigned in the same manner. The current study showed that the average life time number of calves weaned for heifers that calved in the 1st, 2nd, and 3rd 21-day period was 5.4 ± 0.32, 4.5 ± 0.37, and 4.2 ± 0.39, respectively. Retaining percentage rate of period 1 cows was 4.3-17.8 and 2.1-19.1% units greater than those of period 2 and period 3 cows, respectively. Period 1 heifers had the greatest life time produced total cumulative weaning weight (p <0.01) value of 1157 kg/cow, followed by period 2 and period 3 heifers, 947 and 841 kg/cow, respectively. Period 1 cows generated an additional $718 to $1077 in weaned calf revenues over their lifetime. This study suggested that, in western Canada, heifers that calved earlier had greater pregnancy rates, remained in the herd longer, and produced one more calf in their lifetime than those that calved in the later periods.

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)0.000
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 score0.506
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.269
Teacher spread0.261 · 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

Citations7
Published2018
Admission routes4
Has abstractyes

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