Bibliographic record
Abstract
Abstract This chapter introduces cereal grains and their role in the industry, and in life. Their basic composition is reviewed together with their functionality (the capability of a commodity to produce consumer‐acceptable end‐products). By far the most important sphere of vibrational spectroscopy as it applies to cereals is near‐infrared spectroscopy (NIRS). The particular characteristics associated with composition and functionality of individual cereals and other grains for which analysis by NIRS is applicable are outlined. Application of NIRS to industrial aspects of cereals is summarized, including grading and pricing of grains, together with the degree of success achieved in application of NIRS to factors pertinent to the grading and the plant‐breeding selection process. Features of NIR spectroscopy that are peculiar to cereals and other grains are described. The application of NIRS to these materials is complicated by interactions among constituents, which strongly influence wavelength selection and instrument calibration. Spectra of select commodities are used to illustrate some of the anomalies that can occur in the application of NIRS to grains. These include apparent suppression of absorption bands in the natural or “raw” spectra of whole grains, although the bands are distinct when the grains are ground to a meal. Steps in the development of calibration models, and the approaches to their interpretation by statistical processes are explained.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".