S<scp>hort</scp> C<scp>ommunication</scp>: Near infrared reflectance spectroscopy accurately predicts the digestible energy content of barley for pigs
Bibliographic record
Abstract
Zijlstra, R. T., Swift, M. L., Wang, L. F., Scott, T. A. and Edney, M. J. 2011. Short Communication:Near infrared reflectance spectroscopy accurately predicts the digestible energy content of barley for pigs. Can. J. Anim. Sci. 91: 301–304. Density, chicken apparent metabolizable energy (AME), and near infrared reflectance spectroscopy (NIRS) were tested to predict the widely varying swine digestible energy (DE) content of barley. The DE content of 39 barley samples ranged from 2686 to 3163 kcal kg−1 (90% DM) in grower pigs. The R2 between DE content and density (0.14) and broiler chicken AME content (0.18 and 0.56, without and with enzyme, respectively) was low. In contrast, the coefficient of determination to predict swine DE content for ground barley samples using NIRS was respectable for external validation (R2=0.74) and internal cross validation (1-VR=0.79), but more robust calibrations should be developed for commercial application.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.056 |
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".