Three-Dimensional Transient Heat, Mass, and Momentum Transfer Model to Predict Conditions of Canola Stored inside Silo Bags under Canadian Prairie Conditions: Part I. Soil Temperature Model
Bibliographic record
Abstract
Silo bags have been used by Canadian farmers in the last few years to temporarily store cereal grains, pulses, and oilseeds for up to one year. It is difficult to install temperature and moisture cables inside silo bags or conduct sampling because any cutting of the silo bag damages its hermeticity. Mathematical models could be used to predict spoilage during storage, and the accuracy of mathematical models is influenced by soil temperature. In this study, soil temperature models were developed based on the energy balance on the surface of the ground and heat conduction equations (HCE). The surface of the ground was covered with snow during winter and with vegetation during summer. The developed soil models were coupled with the developed three-dimensional transient heat, mass, and momentum transfer models inside silo bags. The developed models were validated using weather data (solar radiation, temperature, relative humidity, wind speed, and snow covering) and temperatures collected inside bulk canola in silo bags with 9.1% and 10.5% moisture contents. The prediction accuracy associated with the HCE models was compared with that associated with Fourier series, which has been used in the literature. The HCE models developed in this study had higher prediction accuracy than the Fourier series. The maximum absolute difference and average absolute difference between the measured and predicted (by the HCE models) canola temperature at 10 cm height of the canola from the bottom of the silo bag were 3.23°C and <1.79°C ±0.04°C, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".