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Record W2313137406 · doi:10.1055/s-0034-1368438

Erbkrankheiten beim Quarter Horse

2014· article· de· W2313137406 on OpenAlexaboutno aff
Bärbel Gunreben

Bibliographic record

Venuepferde spiegel · 2014
Typearticle
Languagede
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsnot available
Fundersnot available
KeywordsArtQuarter (Canadian coin)HorseHumanitiesGeographyArchaeologyGeology

Abstract

fetched live from OpenAlex

All articles of this category Das Quarter Horse ist die zahlenmäßig größte Pferderasse der Welt mit insgesamt über 4,6 Millionen registrierten Pferden. Im Folgenden geht es nicht nur um das Quarter Horse, sondern auch um Paint Horses und Appaloosas , die sehr eng mit dem Quarter Horse verwandt sind und ebenso speziell für das Westernreiten gezüchtet werden. Alle 3 Rassen stammen ursprünglich aus den USA, sind aber seit einigen Jahren auch bei uns in Europa immer häufiger anzutreffen. Aus diesem Grund bekommen die bei den Westernpferden bekannten Erbkrankheiten auch bei uns eine größere Relevanz. Einige sind bereits mittels Gentest nachzuweisen.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0170.005

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 head0.046
GPT teacher head0.345
Teacher spread0.298 · 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
Published2014
Admission routes1
Has abstractyes

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