Use of near Infrared Reflectance Spectroscopy for the Characterization of Wheat and Barley Grain Entering Feedlots in Western Canada
Bibliographic record
Abstract
The first experiment evaluated the use of near infrared reflectance spectroscopy (NIRS) for the nutrient prediction of wheat grain and the factors affecting in vitro dry matter digestibility (IVDMD) and in vitro kinetics of gas production of wheat grain. Wheat samples (n = 75) were selected from three feedlots in Alberta from September 2011 to April 2012 to represent a range in DM, CP, starch, and fat. The prediction models for DM, CP, and starch were tested and the effect of each nutrient on in vitro fermentation parameters were evaluated. A second experiment was conducted evaluating the effects of a barley spectra index on in vitro fermentation parameters and feedlot performance of yearling cattle. Results of the first experiment demonstrate that NIRS can accurately predict (R2 = 0.90) the CP content but not DM or starch (R2 = 0.17 and 0.02, respectively) across a broad range of composition. High DM samples had greater IVDMD (P < 0.05) than low and medium DM samples. Rate of gas production of high starch samples was lower than low starch samples and higher for high CP samples than medium and low CP samples. Results of experiment two indicate that segregating barley by spectra index may improve cattle performance by minimizing variability in substrate supplied to the rumen. Cattle fed LOW, MED, or HIGH spectra index barley had greater DMI (P = 0.02), tended to have greater HCW and live- and carcass adjusted ADG (P = 0.08, 0.09, 0.07, respectively) than cattle fed the unsegregated CON. Likelihood of Yield Grade 1 carcasses was greatest (P = 0.05) in steers fed MED treatment barley. As treatment group increased there was: a linear decrease in DM (P = 0.02); linear increase in CP (P < 0.01); a tendency for a linear decrease in starch (P = 0.07); linear decrease in the color variables brightness and red:green scale (P = 0.02 and 0.04, respectively); and linear increases in 1,000-kernel weight and kernel diameter (P < 0.05).
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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 | 0.000 |
| 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".