A Preliminary Evaluation of Black and Navy Bean Productivity in Virginia
Bibliographic record
Abstract
Black and navy beans (Phaseolus vulgaris L.) are economically important crops for US Agriculture and provide nutritious food for humans. These crops are predominantly grown in Colorado, Idaho, Michigan, Nebraska, North Dakota, and Washington states. We are interested in introducing these crops in Virginia as alternative summer crops. Four cultivars each of black (Eclipse, T-39, Zenith and Zorro) and navy bean (Alapena, Avalanche, Norstar, and Vista) were grown in the field at Randolph Farm of Virginia State University in Ettrick, Virginia during 2016. We planted these twice (May 26 and June 30) by using two inter-row spacings (37.5 and 75 cm). Results indicated that yields of black bean (1691 kg/ha) didn't differ significantly from that of navy bean (1402 kg/ha). Effects of cultivars and row spacings were not significant. Seed yield for May planting date (883 kg/ha) was significantly lower than that for June planting (2210 kg/ha). Concentrations of protein, P, K, Ca, Mg, S, Al, B, Cu, Fe, Mn, fructose, glucose, sucrose, raffinose, stachyose, verbascose, total sugar, insoluble dietary fiber, and total dietary fiber in black and navy bean seeds were not different. Black bean had significantly higher soluble dietary fiber concentration (4.46%) as compared to navy bean (3.68%). Nutritional quality traits of black and navy bean seed produced in Virginia compared well with values in the literature. Based on desirable seed yield levels and nutritional quality, it was concluded that black and navy bean are potential new/alternate crops for Virginia and adjoining areas in the mid-Atlantic region of United States of America.
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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".