Bibliographic record
Abstract
Adzuki bean is a niche market, high-value field crop suited to the temperate growing regions of the world. Adzuki bean lacks early season vigour and thus early season weed control is critical for profitable production. Efficacious application of preplant incorporated (PPI), preemergence (PRE), and, to a lesser degree, postemergence (POST) herbicides have been documented, however, the number of registered herbicides is currently limited due to the sensitivity of adzuki bean crops. In addition to the currently registered products, the literature shows the potential utility of cloransulam-methyl or halosulfuron applied PPI and (or) PRE, and imazamox or acifluorfen applied POST in adzuki. Furthermore, growers should avoid atrazine, metribuzin, EPTC, pethoxamid, pyroxasulfone, clomazone, flumioxazin, sulfentrazone, alachlor, dimethenamid-P, and S-metolachlor applied PPI and (or) PRE, and halosulfuron, thifensulfuron-methyl, and bentazon applied POST, due to poor adzuki bean tolerance to these herbicides. While crop tolerance research represents a growing body of work, there is a paucity of available weed control data to assist growers. The persistence of volunteer adzuki bean is a significant hurdle for adzuki bean growers. However, crop and herbicide mode-of-action rotation, in combination with early-season [PPI and (or) PRE] control, have demonstrated success. There is an ongoing need to evaluate weed control and expand the number of registered herbicides for adzuki bean growers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".