Utilization of Near Infrared Reflectance Spectroscopy for the Evaluation and Characterization of Barley in Western Canada
Bibliographic record
Abstract
The first study evaluated near infrared reflectance spectroscopy (NIRS) for the determination of barley silage DM on as-is samples using either a commodity specific or broad based equation. A second study was conducted to evaluate a commercial NIRS prediction equation for barley grain, examining the nutrients of DM, CP and starch. Barley samples were selected as HIGH, MID or LOW for each nutrient group and the equation was tested using all samples or only the selected samples. Finally, a third study was conducted to evaluate NIRS as a selection tool for barley grain and the relationship between nutrient composition and digestion kinetics. The results of the first study indicated that NIRS accurately predicts the DM of as-is barley silage (R2 = 0.98, p < 0.05) using either a commodity specific or broad based equation. The second experiment indicates NIRS can accurately predict the DM and CP (R2 > 0.50, p < 0.05), however did not accurately predict starch content of barley grain (R2 �� 0.21, p < 0.05). The third experiment indicates that NIRS holds promise as a selection tool for barley grain quality and a relationship exists being nutrient content and digestion kinetics. There was a significant relationship between the DM content of the sample and the rate of fermentation with LOW DM samples having a faster rate of fermentation than the MID and HIGH (p < 0.05). Gas production of LOW DM samples was greater between 8 and 23 hours of incubation compared to the HIGH and MID (p < 0.05). The MID CP had greater gas production (mL/g of substrate DM, p �� 0.05) than the HIGH range, with LOW being intermediate. Correlations between the NIRS and lab determined chemical constituents and the gas production kinetics were examined. DM was negatively correlated (p �� 0.05) with k and lag when measured with NIRS or in a lab, and CP was significantly (p �� 0.05) negatively correlated with cumulative gas production (NIRS r = -0.31, lab r = -0.31), k (NIRS r = 0.48, lab r = 0.47), and lag (NIRS r = 0.30, lab r = 0.37).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".