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Record W7008106094

Aspetti clinici e genetici della displasia retinica del Labrador Retriever

2016· article· it· W7008106094 on OpenAlexaboutno aff

Bibliographic record

VenueElectronic Theses and Dissertations Repository (University of Pisa) · 2016
Typearticle
Languageit
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsLabrador RetrieverCongenital diseaseGenetic diagnosisSequence (biology)
DOInot available

Abstract

fetched live from OpenAlex

Lo scopo di questa tesi è stato quello di valutare gli aspetti clinici e studiare la trasmissione ereditaria della displasia retinica in un gruppo di cani di razza Labrador Retriever, sulla base di una valutazione retrospettiva effettuata su un periodo di 105 mesi (Gennaio 2007-Novembre 2016). Sono stati visitati 206 soggetti di cui 105 erano femmine (50,9%) e 101 maschi (49,1%). L’età media dei soggetti visitati è di 16,62 mesi (range 6-116 mesi). Sono risultati affetti da displasia retinica 33 soggetti (15,9%). La displasia retinica focale monolaterale è stata riscontrata nel 54,2% degli animali affetti, la displasia retinica focale bilaterale nel 2,8%, la displasia retinica multifocale monolaterale nel 14,3%, la displasia retinica multifocale bilaterale nel 20%, la displasia geografica monolaterale nel 5,7% e la displasia geografica bilaterale nel 2,8% dei casi. In base all'analisi genetica la trasmissione della displasia retinica focale/multifocale sembra essere autosomica recessiva. La displasia retinica geografica non sembra essere correlata genotipicamente alle altre forme.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.006
GPT teacher head0.213
Teacher spread0.208 · 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
Published2016
Admission routes1
Has abstractyes

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