Thermal imaging for potential use in cereals and oilseeds handling
Bibliographic record
Abstract
IX 1.To determine the non-uniformity of heating on the surface of grain after microwave treatment using a pilot-scale drier.2. To determine the germination percentage and fat acidity value (FAV) of the wheat samples collected from the high temperature and the normal temperature regions of bulk grain after microwave treatment.3. To determine the capability of thermal imaging to detect a hot spot in a stored grain silo.4. To determine the efficiency of a thermal imaging technique to identify western Canadian wheat classes by bulk sample analysis.5. To determine the efficiency of a thermal imaging technique to detect the presence of Cryptolestes ferrugineus (Stephens) inside wheat kernels at six developmental stages (four larval, pupal and adult). Thesis OutlineChapter 1 describes the general introduction about the Canadian grain industry, infrared thermal imaging and objectives of this research.Chapter 2 deals with the review of literature pertaining to each objective, and theory and agricultural applications of infrared thermal imaging.Chapters 3 to 7 are presented in paper format, and the details are explained in Table 1.1.The overall conclusions and recommendations are discussed in Chapter 8. Table 1.
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".