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Record W3207752452 · doi:10.15547/tjs.2020.s.01.004

DAMAGES OF THE KEEL BONE IN LAYING HENS – OVERVIEW OF THE ETIOLOGICAL ASPECTS

2020· article· en· W3207752452 on OpenAlexaboutno aff
K. Uzunova, L Lazarov

Bibliographic record

VenueTrakia Journal of Sciences · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsKeelDamagesProductivityWelfareLimitingAnimal welfareAgricultureBusinessBiologyEcologyGeographyEngineeringEconomic growthEconomicsMarket economyPolitical scienceLaw

Abstract

fetched live from OpenAlex

The damages of the locomotor system and the skeletal system, in particular, are one of the major problems in the industrial poultry farming. The topic for the pathological changes in the keel bone in oviparous birds has become especially popular among scientists and researchers in recent years. The high incidence of keel bone damage (KBD) of laying hens in industrial complexes is one of the biggest welfare problems facing the industry. They lead to disturbance of the animal welfare, causing pain, limiting the ability to move and to perform the characteristic behaviour of the species. This in turn causes a decrease in productivity and unacceptably large losses not only for the individual producer but also for the whole sector. The problem with the KBD is widespread in Switzerland, Great Britain, the Nederland, Belgium, Germany, and Canada. Different genetic lines of laying hens are affected, as well as all types of breeding systems. In general, the etiological factors are reduced to three main groups – genetic predisposition, unbalanced diet and imperfections in housing systems. The causes and influencing factors of KBD remain unknown to the research community - a circumstance that seriously complicates the development of effective strategies to reduce their occurrence and severity.

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)0.000
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 score0.925
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.136
GPT teacher head0.285
Teacher spread0.150 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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