Prédiction de la qualité de la viande de ruminants
Bibliographic record
Abstract
Meat & Livestock Australia and Meat Standards Australia and INRA have organized an International meeting on Beef and Lamb carcass grading to underpin consumer satisfaction. The 2 day meeting consisted of 19 presentations centred on the theme that modern beef and lamb products must meet the expectations of consumers who purchase red meat to cook it as a meal solution. The focus was based around the Meat Standards Australia (MSA) grading platform which is designed as a sensory or eating quality grading system for underpinning a cooked meal performance that is matched to the occasion and requires no specialist knowledge by the consumer. This workshop unanimously supported the need for evidence based systems to underpin eating quality for lamb and beef in order to keep consumers purchasing products that are higher in price than the white meat competitors. Registrations were received from 80 people covering 17 countries (Australia, Brazil, Canada, China, Czech Republic, Denmark, France, Italy, Japan, Republic of Ireland, Poland, Portugal, South Africa, Spain, Thailand, United Kingdom, United States of America) creating a dynamic workshop atmosphere. In order to drive and focus the recommendations which were discussed at the end of the workshop, it was agreed to establish a working group of current collaborating countries that would be open with respect to new partners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".