Assessment of fan control strategies for in-bin natural air-drying of wheat in Western Canada.
Bibliographic record
Abstract
Grains (common term referring to cereal grains, oilseeds, pulses) are usually harvested at high moisture content and then dried to straight grade (dry) or safe storage moisture levels. Grain drying in freestanding, corrugated galvanized steel or welded steel bins using natural air is the most cost effective drying method with optimum grain quality. Adverse weather conditions and inappropriate fan control strategies may result in poor drying, higher drying cost (electricity and fuel), and grain spoilage. Several traditional fan control (continuous ON, only Day ON, only Night ON) and automated fan control (Natural Air Drying (NAD) and Self-Adapting Variable Heat (SAVH)) strategies were investigated using IntegrisPro model software (OPIsystems Inc.©) for their drying potential for wheat using 30 years of historical weather data (1983-2012) from 14 locations in Western Canada (Alberta, Manitoba, and Saskatchewan provinces) which cover nearly 75% of Canada’s farming area. Effects of initial wheat moistures (20, 18, and 16%), start dates (August 20th, September 1st, September 15th, and October 1st), locations, airflow rates (0.52, 0.78, and, 1.04 m3min-1t-1) and supplemental heat on drying performance were studied. High moisture wheat (18-20%) can only be dried effectively with sufficient airflow rate (0.78, and, 1.04 m3min-1t-1) and early drying start date (September 15th or earlier). Without automated control, Continuous ON fan control was a better control strategy. The SAVH control strategy gave the optimized results in terms of fan run hours, target moisture, moisture spread, and shrink (over-drying). The Night ON fan control strategy gave the poorest drying results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".