A THREE-DIMENSIONAL, ASYMMETRIC, AND TRANSIENT MODEL TO PREDICT GRAIN TEMPERATURES IN GRAIN STORAGE BINS
Bibliographic record
Abstract
A three-dimensional, transient, combined model (headspace model + soil model + conduction model in bulkgrain) was developed to predict grain temperatures in a granary. Different meshes (mesh refinement in the whole domain orat the boundary) including linear and hybrid (linear and quadratic) elements were used to simulate grain temperatures. Predictionaccuracies of temperatures produced by the different meshes were compared, and the model was validated using measuredtemperatures in two flat bottom bins (3.76 m diameter and 5.5 m high filled with wheat up to 3 m) located side by sidein the north-south orientation near Winnipeg, Manitoba. Grain temperatures predicted by the model were in close agreementwith the measured temperatures throughout a 21-month test in the two bins. By using a hybrid element mesh (mesh refinementat the boundary), the mean, standard error, and maximum of the absolute difference between the measured and predicted temperaturesin the south bin were 2.2C, 0.4C, and 7.0C, respectively. The mean, standard error, and maximum of the absolutedifference predicted by a linear element model (88 linear elements each layer) in the south bin were 2.1C, 0.3C, and 6.3C,respectively. Including a headspace model improved the prediction accuracy of the conduction model at the top of the grainbulk. Mesh refinement only at the boundary produced a homogeneous distribution of errors in the whole domain; however,mesh refinement in the whole domain gave higher errors at the walls than at the center of the bins. Considering the increasedcomputer time and slightly improved accuracy by mesh refinement at the boundary, a uniform mesh with mesh refinement inthe whole domain was preferable for predicting grain temperatures in an entire grain bin.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".