Management strategy evaluation of stored grain using global sensitivity analysis: Part I – Allowable maximum variations of temperatures and moisture contents of canola
Bibliographic record
Abstract
To maintaining grain quality and quantity, stored grain should be stored at low temperatures and dry moisture contents with minimum variations of temperature and moisture content. Global sensitivity analysis was conducted to characterize these allowable variations by simulating different ranges of temperature, moisture content, and initial germination with different distributions of the simulated temperatures, moisture contents, and initial germinations. Mathematical models published in literature for predicting canola germination were coded in Symbiology and Simulink for conducting these simulations. Random and normal distributions of temperatures, moisture contents, and initial germinations were simulated. Simulations covered ranges of temperature (5–40 °C), moisture (6–12 %, wet basis), and initial germination (92–98 %) variations to assess their effects on germination reduction. Simulation results and global sensitivity analysis concluded that the allowable maximum variations of moisture content in stored canola granaries depended on the variation of temperatures, and vice versa. Any moisture variation would reduce the recommended safe storage time. The allowed maximum standard error of moisture contents for one third of the recommended safe storage time was 0.5, 0.5, and 0.1 percentage point at 18 ± 3, 23 ± 3, and 28 ± 3 °C, respectively. These findings provide critical guidelines for optimizing canola storage conditions to minimize spoilage risk. • Global SA and simulation were conducted to answer research questions. • Allowable variations of moisture depended on temperature variations, vice versa. • High initial germination at the beginning of the storage time had minimum effect. • The allowed maximum temperature was 23 ± 3 °C for safely storing canola. • Allowed standard error of moisture content was 0.5 percentage point at 23 ± 3 °C .
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".