Visible and near Infrared Spectroscopy of Beef <i>Longissimus Dorsi</i> Muscle as a Means of Dicriminating between Pasture and Corn Silage Feeding Regimes
Why this work is in the frame
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Bibliographic record
Abstract
Near infrared (NIR) reflectance spectroscopy was used as a tool to classify beef muscle samples according to their feeding regime. Seventy-eight beef longissimus dorsi muscle samples both intact and minced were scanned in a NIRS 6500 instrument (NIRSystems, MD, USA) in reflectance. A dummy regression technique was developed to differentiate beef muscle samples, which originated from beef feed exclusively on pasture or/and mainly on corn silage feeding regimes. Ninety percent of the pasture-fed beef muscle samples were correctly classified using principal component regression (PCR) and 86% of beef fed on corn silage were correctly classified. Both muscle chemical composition and physical characteristics explained the classification results. The results in the present study showed the potential of muscle optical properties for classification and traceability of meat muscles in the food chain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it