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Record W7037914402

First stage of the contractualization between veterinarians and dairy cattle farmers: market study

2023· dissertation· en· W7037914402 on OpenAlexaboutno aff

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2023
Typedissertation
Languageen
FieldMedicine
TopicPharmaceutical Quality and Counterfeiting
Canadian institutionsnot available
Fundersnot available
KeywordsRural populationQuarter (Canadian coin)Rural developmentStage (stratigraphy)
DOInot available

Abstract

fetched live from OpenAlex

Le marché du soin vétérinaire dans le milieu rural fait face à de nombreux enjeux qui le façonnent. Les vétérinaires et les éleveurs rencontrant de nombreuses problématiques. Des besoins se dégagent fortement de ses problématiques. Afin de palier à ses enjeux et de répondre à ses besoins, des vétérinaires ont développé une nouvelle manière de travailler : La contractualisation. Ce travail a pour but dans un premier temps de faire ressortir les problématiques et les besoins des vétérinaires et des éleveurs dans le monde qui les entoure. Dans un second temps, ce travail cherche à savoir si la contractualisation est une solution de réponse aux enjeux du marché des soins vétérinaires. C’est en réalisant une étude de marché que des réponses ont été apportées. L’objectif de cet écrit est de donner une idée aux vétérinaires praticiens d’une solution possible à appliquer dans leurs clientèles pour répondre à leurs besoins mais aussi à ceux de leurs éleveurs.

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)0.009
metaresearch head score (Gemma)0.020
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 score0.025
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0210.001

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 head0.042
GPT teacher head0.321
Teacher spread0.278 · 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
Published2023
Admission routes1
Has abstractno

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