Using blood near infrared spectra from steers to classify fat and meat samples with low or high levels of vaccenic acid
Bibliographic record
Abstract
Near infrared spectroscopy on steer blood was tested to discriminate between subcutaneous fat and longissimus thoracis samples with low or high levels of vaccenic acid. One day prior slaughter, blood samples from steers were harvested and near infrared spectra were collected on both whole blood and red blood cells. At slaughter, samples of subcutaneous fat and longissimus thoracis were collected and vaccenic acid content was analyzed by gas chromatography. Partial least squares discriminant analyses based on whole blood and red blood cell near infrared spectra were applied to classify subcutaneous fat and longissimus thoracis samples according to their content of vaccenic acid (low or high). Based on the results from the partial least squares discriminant analyses, subcutaneous fat and longissimus thoracis samples with low or high content of vaccenic acid could be discriminated from the blood near infrared spectra with accuracy from 74 to 95%. Application of near infrared spectroscopy technology on steer blood may have potential as early screening of live animals based on low or high levels of vaccenic acid in subcutaneous fat or longissimus thoracis.
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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 | 0.000 | 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 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".