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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".