The Hygroscopic Properties and Sorption Isosteric Heats of Different Chinese Wheat Types
Bibliographic record
Abstract
The moisture sorption isotherm data of fourteen Chinese wheat varieties were determined using the static gravimetric method at five different temperatures (10, 20, 25, 30 and 35 °C) and relative humidity ranging from 11.3 to 96%. Eight models, namely Brunauer-Emmett-Teller, CAE, Chen-Clayton, Modified-Chung-Pfost (MCPE), Modified-Henderson, Modified-Guggenheim-Anderson-deBoer, and Modified-Oswin and Strohman-Yoerger, were used to fit the sorption data. MCPE shows the best fitting results. A significant hysteresis effect was found between wheat desorption and adsorption isotherm at lower ERH, but the similar hygroscopic properties remained for different wheat types like hard vs. soft, red vs. white, and winter vs. spring, respectively. The experimental results show that the isosteric heats for both wheat adsorption and desorption, and all the sorption heats for different wheat types decrease rapidly with increasing seed moisture initially, however, after the moisture is more than 15% w.b. they decrease tardily with increasing moisture content. The isosteric heats of wheat desorption were considerably higher than those of adsorption below 17.5% m.c., but the similar sorption isosteric heats were found for wheat types like hard vs. soft, red vs. white, or winter vs. spring, respectively. It is concluded that the wheat grains from different types have similar hygroscopic properties and sorption isosteric heats and can be synchronously dealt with during physical control in storage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".