Physical Properties of White and Black Beans (<i>Phaseolus vulgaris</i>)
Bibliographic record
Abstract
Abstract. Physical properties including physical dimensions (length, width and thickness), 1000 kernel weight, bulk density, true density, angles of repose, and angles of friction against four structural surfaces of white beans (or navy beans) and black beans ( L.) were measured at 12.1 ±0.3%, 14.0 ±0.0%, 16.0 ±0.2%, and 18.0 ±0.4% moisture contents (wet basis). Physical dimensions of beans were measured using a Vernier caliper and a digital imaging system and the results were compared. Physical dimensions and 1000 kernel weight of beans increased with the increase in moisture content from 12% to 18%. Physical dimensions of both beans measured using the Vernier caliper and the digital imaging system were similar except the thickness of the black bean at 12%, 14%, and 18% moisture contents. Bulk densities and true densities of both beans linearly decreased with the increase in moisture content. Emptying angle of repose was larger than the filling angle of repose for both beans. The filling angles of repose of both beans at 14% moisture content was significantly higher than that at other moisture contents. The largest angle of friction was against the wood-floated concrete followed by the steel-troweled concrete, galvanized steel, and plywood surface for both beans. Angle of friction against galvanized steel and plywood for both beans increased with increase in moisture content, whereas, angle of friction against wood-floated concrete and steel-troweled concrete decreased with increase in moisture content. Keywords: White beans, Black beans, Physical dimensions, Digital imaging system.
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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 | 0.000 |
| 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".