Carbohydrates in alfalfa-timothy mixtures predicted with near infrared reflectance spectroscopy equations developed for single species
Bibliographic record
Abstract
Our objective was to evaluate the feasibility of using near infrared reflectance spectroscopy (NIRS) equations previously developed with a calibration set that included samples of both timothy and alfalfa to predict carbohydrate fractions in mixed samples of both species. Timothy and alfalfa mixed samples were prepared with the alfalfa proportion ranging from 0 to 100%, with increments of 4%. With previously developed NIRS equations based on samples of single species of timothy and alfalfa, concentrations of total ethanol soluble carbohydrates (TESC), starch, and neutral detergent soluble carbohydrates (NDSC) of the mixed samples were predicted successfully, but concentrations of organic acids (OA) and neutral detergent soluble fiber (NDSF) were unsuccessfully predicted. Adding 13 mixed samples to the initial calibration set of around 110 samples of pure timothy and alfalfa samples improved the accuracy of already successful predictions for TESC, starch, and NDSC, and resulted in a successful prediction for NDSF in timothy and alfalfa mixtures.Key words: Near infrared reflectance spectroscopy, sugars, Phleum pratense, Medicago sativa
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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 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".