Measurement of Water Diffusivities in Barley Components Using Diffusion Weighted Imaging and Validation with a Drying Model
Bibliographic record
Abstract
Diffusion-weighted magnetic resonance imaging was used to determine water diffusion coefficients (D) in hull-less barley kernel components (endosperm and embryo) at 20.5±0.5°C. The D values in barley components were time-dependent and restricted in nature as indicated by the decrease in the apparent diffusion coefficient with increasing diffusion time (from 3 to 25 ms). A four-parameter Padé approximation model was used to estimate D and pore geometry (pore surface area–to-volume ratio, pore size, porosity, electrical conductivity and permeability of water) of the barley components after long diffusion time (t → ∞) using data obtained during a relatively short period of diffusion. The D of embryo and endosperm were 2.2±0.07 × 10−5 mm 2 Czuchajowska , Z. ; Klamczynski , A. ; Paszczynska , B. ; Baik , B.-K. Structure and functionality of barley starches . Cereal Chemistry 1998 , 75 ( 5 ), 747 – 754 .[Crossref] , [Google Scholar]/s and 1.0±0.10 × 10−5 mm 2 Czuchajowska , Z. ; Klamczynski , A. ; Paszczynska , B. ; Baik , B.-K. Structure and functionality of barley starches . Cereal Chemistry 1998 , 75 ( 5 ), 747 – 754 .[Crossref] , [Google Scholar]/s, respectively. These D values were used to simulate moisture and temperature patterns during the drying of a barley kernel using a two-dimensional simultaneous heat and moisture transfer model and compared with literature D values for validation purposes. Based on the comparison, the D values of barley components obtained from our study can be used to develop realistic models of water transport in barley during different postharvest processing operations (e.g., drying, kilning, steeping).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".