Effect of different sub-zero temperatures and relative humidities on moisture content and germination of stored canola
Bibliographic record
Abstract
Storage of high-moisture canola remains a major challenge due to mold growth resulting in seed deterioration. While equilibrium moisture content (EMC) models have been widely applied to above-zero storage and drying conditions, no research has quantified EMC isotherm and germination under sub-zero temperatures. Isotherm and germination of canola, conditioned to 14–18 % moisture contents, were tested at temperatures from −25 to +30 °C and relative humidities (RHs) between 15 and 94 %. Multiple EMC models were evaluated for their predictive accuracy across the studied temperatures and RHs. The main factor influencing EMC of canola from −5 to −20 °C was the RH, and temperature had minimal influence. Both temperature and RH influenced the EMC when temperature was ≤ −20 °C. When RH was higher than 75 %, EMC of canola had an exponential increase with the increase of RH and/or decrease of temperatures. Seed germination remained high (≥80 %) at −5 to −15 °C, and RH had a minimum influence on the germination. At −20 and −25 °C and higher than 40 % RH, canola germination was reduced to less than 40 % after EMC was reached, indicating severe damage to the canola seeds. Peleg model yielded the best fit to the isotherm data at different sub-zero temperature ranges. These findings offer essential guidance for sub-zero grain storage and drying strategies, with implications for preserving seed viability and reducing post-harvest losses.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".