MétaCan
Menu
← Back to cohort
Record W6977847110 · doi:10.7939/r3-7faf-2310

Identification of feed intake traits related to beef reproductive improvement

2022· dissertation· en· W6977847110 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2022
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsnot available
Fundersnot available
KeywordsResidual feed intakeDry matterHerdBeef cattleReproductionBody weightIdentification (biology)Feed conversion ratio

Abstract

fetched live from OpenAlex

Reproductive efficiency in the Western Canadian beef cow herd has not improved over the past 3 decades, despite consistent and measurable improvement in several related areas of production. The general objective of this thesis was to evaluate the relationships between early-observation traits (feeding behaviour), weight change, and reproductive efficiency in beef cows. Estimates of total dry matter intake and the relationship between residual feed intake (adjusted for body composition; RFIFAT) observed in heifers and mature cows were also evaluated as traits of interest and use to the Canadian beef industry. Chapters 3 and 4 investigated the relationship between feeding behaviours and reproductive efficiency and provided phenotypic and genetic correlations that may be of use in selecting heifers with greater reproductive potential if those heifers have feeding behaviours reported. Feeding behaviour and reproductive efficiency were both correlated with dry matter intake; feeding behaviours that promote dry matter intake may be useful in the identification of heifers with greater reproductive potential. Chapter 5 evaluates the usefulness of tracking cow weights over time and the usefulness of comparing cow weights to an idealized growth curve estimation. Generally, cows that gained weight over time were more likely to be culled for reproductive failure, and the same was true for cows that were heavier than their estimated body weight. Producers may be able to use weight-monitoring technology currently available and in development to identify cows that abort their calves after a positive pregnancy evaluation and remove them from their herd at a time when feed resources are expensive. Chapter 6 was a comprehensive and unique estimation of cow dry matter intake over time, including energy estimates based on birth, weaning, residual feed intake tests as a heifer and as a cow, and subsequent calving events with associated energy expenditures for milk production. These estimates were compared to genomic retained heterozygosity, breed composition, and winter-feeding environment to evaluate the effects of those variables on dry matter intake predictions. Chapter 7 investigated the relationship between heifer residual feed intake and residual feed intake observed in the same animals as mature cows. Residual feed intake in heifers can be used to select cows that maintain a proportion of their efficiency observed as heifers, and ultimately provides evidence that the selection of feed-efficient heifer calves as replacement animals should result in a more efficient mature cow herd. This thesis provided evidence to support the selection of heifer replacements using feeding behaviours observed during a feed intake test, the use of weight monitoring technology to identify cows that may have had reproductive issues. This thesis also provided some of the first estimates of dry matter intake over the course of the production cycle in a large number of animals under normal production environments and provided estimates of the relationship between heifer and cow residual feed intake.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.327
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.195
Teacher spread0.188 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueUniversity of Alberta Library→Same topicReproductive Physiology in Livestock→French-language works237,207→