MétaCan
Menu
Back to cohort
Record W2134005903 · doi:10.5539/mas.v8n6p170

Modern Intensive Dairy Beef Production Systems in Russia

2014· article· en· W2134005903 on OpenAlexvenueno aff
Г. П. Легошин, Т. Г. Шарафеева, А. П. Мамонов

Bibliographic record

VenueModern Applied Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Health
Canadian institutionsnot available
Fundersnot available
KeywordsBreedAnimal scienceDry matterForageBiologyCarcass weightFeed conversion ratioBody weightAgronomy

Abstract

fetched live from OpenAlex

The objective of this study was to determine the biological and economic efficiency of growing-finishing young dairy bulls slaughtered at different live weights under current consumer´s requirements and market conditions. Experiment was conducted with bull calves of Black and White breed with high level (>85%) Holstein blood (n=60). All animals were indoor housed and fed on diets consisting of forage (55 percent on dry matter basis) and concentrate (45 percent) with flat daily live weight gains 900-1000g. The overall findings for the dairy calf to beef system can be summarized as follows. Under current conditions optimum slaughter weights of young bulls are 500-550 kg at the ages of 17-18 months. Biological efficiency as measured by carcass weight production per animal, carcass and meat qualities, increased with increasing of slaughter weights from 400 to 550kg. The same pattern has occurred for net return, but profitability was unchanged. Production systems with light slaughter live weights require more number of cattle by 14-41 percent to produce the same carcass weights. On the other hand feed conversion of the bulls slaughtered at light live weights was higher. Meat potential of Black and white breed with high proportion of Holstein blood is good enough to produce quality lean carcasses.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.240
Teacher spread0.211 · 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 designBench or experimental
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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicAnimal Nutrition and HealthFrench-language works237,207