MétaCan
Menu
Back to cohort
Record W2514512994 · doi:10.12737/20339

THE EFFECT OF THE HEIGHT AT THE RUMP ON A LIVE WEIGHT AND AVERAGE DAILY GAINS

2016· article· en· W2514512994 on OpenAlexaboutno aff
Хакимов, Ismagil Khakimov, Живалбаева, Almagul Zhivalbaeva

Bibliographic record

VenueBulletin Samara State Agricultural Academy · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Health
Canadian institutionsnot available
Fundersnot available
KeywordsSacrumAnimal scienceWeight gainCorrelation coefficientRumpMathematicsBody weightBiologyStatisticsAnatomyEndocrinology

Abstract

fetched live from OpenAlex

The purpose of the study – improvement of breeding and productive qualities of Hereford cattle by insemination bulls form the canadian selection. It is established that in young Hereford beef cattle between live weight and height at the sacrum and between the height at the sacrum and average daily gains there are mainly medium and high positive correlation (from 0.35 to 0.73) . Regression analysis has shown that the change in height in the sacrum on 1 cm, causes the increase of live weight of animals from 1.55 kg in animals of different lines and produktivity gain of calves from 7.67 g to amounted to 23.87g. The highest coefficient of the time average correlation coefficient of between the height at sacrum and live weight setlen in the group of heifers, obtained a bull from the absolute 49S, and the highest co-factor regression between these two traits in heifers obtained from a bull of a Wide Load 391W. The same trend holds when comparing groups correlation coefficient and regression between the height at the sacrum and average daily gain. It is noted that calves of all groups are well adapted to local conditions, and have high gain.

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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.012
GPT teacher head0.222
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 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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueBulletin Samara State Agricultural AcademySame topicAnimal Nutrition and HealthFrench-language works237,207