Discrimination of beef dark cutters using visible and near infrared reflectance spectroscopy
Bibliographic record
Abstract
Prieto, N., Lopez-Campos, O., Zijlstra, R. T., Uttaro, B. and Aalhus J. L. 2014. Discrimination of beef dark cutters using visible and near infrared reflectance spectroscopy. Can. J. Anim. Sci. 94: 445–454. This study examined the potential of visible and near infrared reflectance spectroscopy (Vis-NIRS) to segregate dark cutters from normal beef. One hundred and twenty beef carcass sides were selected from a slaughter plant by experienced graders according to their carcass grade: 60 A grade carcasses (normal) and 60 B4 grade carcasses (dark cutters). At approximately 48 h post mortem, a 2.5-cm-thick steak (at ∼7/8th thoracic vertebrae) was removed, vacuum packaged and frozen at −25°C until spectra collection. Four Vis-NIRS analyses were performed with different instruments and sample presentation. Partial least squares discriminant analysis based on Vis-NIR spectra correctly classified 95% of the intact non-oxygenated beef samples from both A and B4 grade carcasses using a portable LabSpec®4 spectrometer...
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".