Physical Properties of Finola® Hemp Seeds: Clean and Containing Dockages
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Bibliographic record
Abstract
Abstract. Physical properties of hemp seeds (, L., cultivar Finola ® ) with 5%, 10%, and 15% foreign materials and dockages (FMD) at 9%, 12%, and 15% moisture contents (wet basis) were measured. The measured and determined physical properties include physical dimensions (length, width, thickness, kernel volume, sphericity, and average surface area), density (bulk and true density), porosity, weight of 1000 kernels, filling and empting angles of repose, and coefficients of friction on surfaces of plywood, galvanized steel, steel-troweled concrete, and wood-floated concrete. The mean value of the length, width, and thickness of the 50 kernels had a linear relationship with the increase of moisture content; however, this was not true for individual kernels. Bulk density of seeds at any FMD level decreased linearly or quadratically with the moisture content increase, while the true density had an opposite trend. Increasing FMD significantly increased the bulk density at any moisture content, and there were variations for the true density. There were gaps between the seed coat and cotyledon, and these gaps became smaller or disappeared when the seed moisture content was increased. This phenomenon resulted in the same average surface area, same volume, and exponential increase in porosity to 58.5% with the increase of moisture content. Increased FMD significantly decreased the porosity at any moisture content. Emptying angle of repose increased with the increase of moisture contents and percentage of FMD. Higher variability was observed in the filling angle of repose than in the emptying angle of repose. The coefficient of friction was the largest against wood-floated and steel-troweled concrete, followed by galvanized steel, and the smallest was against the plywood surface. Keywords: Angle of repose, Density, Foreign material and dockage (FMD), Friction against structural surface, Hemp seed, Physical dimensions.
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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 | 0.000 | 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) | 0.000 | 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 it