Sorption characteristics of red lentils as affected by postharvest conditions.
Bibliographic record
Abstract
Adsorption and desorption characteristics of three varieties of red lentils in commercial production (Robin, Blaze, and Redberry) were measured in a dynamic set-up where: (i) freshly harvested lentils of different initial moisture content, (ii) lentils that were exposed to successive rewetting and drying cycles, and (iii) lentils exposed to successive freeze/thaw cycles were placed on a set of stacked trays in air tight systems. The experiments were conducted for the air temperature range between 5 and 30°C (with a step of 5°C) and five relative humidity (RH) values to represent a typical storage range. The Modified Halsey equation was found most suitable for the description of lentil moisture relationships and a non-linear regression procedure was performed on both the adsorption and desorption data collected for each variety and treatment combination. The coefficient of determination for non-linear regression (R2) ranged between 0.952 and 0.982, while the standard error of the estimated relative humidity value was within 2.8 to 4.2%. Successive wetting and drying of lentils had little or no effect on the EMC-ERH (equilibrium moisture content - equilibrium relative humidity) characteristics of CDC Robin varieties and a significant change in EMC-ERH was observed for CDC Redberry and CDC Blaze above 60% RH. There was a significant difference between the fresh and freeze/thaw EMC samples for all three red lentil varieties studied. For each lentil variety, the predicted EMC value for the freeze/thaw treated lentils was lower than the predicted EMC value for freshly harvested lentils at a given ERH. The difference in EMC values between the two treatments was most significant at high RH levels.
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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 | 0.000 | 0.000 |
| 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".