Short Communication: An investigation of the use of near infrared reflectance spectroscopy to predict the energy value of meat and bone meal for swine
Bibliographic record
Abstract
Olukosi, O. A., Paton, N. D., Van Kempen, T. and Adeola, O. 2011.Short Communication:An investigation of the use of near infrared reflectance spectroscopy to predict the energy value of meat and bone meal for swine. Can. J. Anim. Sci. 91: 405–409. The feasibility of using near infrared reflectance spectroscopy (NIRS) for predicting metabolizable energy of meat and bone meal (MBM) for swine was investigated. Thirty-three MBM samples were analyzed for chemical composition and their metabolizable energy content was determined in metabolism assays. Near infrared reflectance spectroscopy calibrations were developed for gross and metabolizable energy of the samples. Coefficients of determination for calibration and cross-validation were greater for gross energy compared with metabolizable energy. Poorer prediction of metabolizable energy by NIRS may be due to sources of variation unaccounted for by NIRS. It was concluded that NIRS is feasible for predicting gross energy but not metabolizable energy of meat and bone meal.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".