Adzuki bean [<i>Vigna angularis</i> (Willd.) Ohwi & Ohashi], oilseed radish (<i>Raphanus sativus</i> L.), and cereal rye (<i>Secale cereale</i> L.) as living mulches with and without herbicides to control annual grasses in sweet corn (<i>Zea mays</i> L.)
Bibliographic record
Abstract
Annual grasses are difficult to control in sweet corn in Canada due to the scarcity of registered herbicides with grass activity. In addition to the potential soil health benefits, over-seeding living mulches into the cropping system may help sweet corn growers improve annual grass control by increasing competitive ground cover. To test this hypothesis, trials were established at three locations in Ontario and Quebec, Canada, in 2008 and 2009. At each location, sweet corn was over-seeded at the 4–6 leaf stage with one of three living mulches alone or in combination with an herbicide. The living mulch/herbicide pairings were adzuki bean (linuron + S-metolachlor), cereal rye (saflufenacil), and oilseed radish (pendimethalin). All living mulch treatments were compared with an untreated control and an industry standard (S-metolachlor/atrazine). When sweet corn was over-seeded with living mulches alone, the most effective annual grass control was provided by the cereal rye. The least effective living mulch was adzuki bean, but the combination of adzuki bean plus a herbicide was the most effective for annual grass suppression. The final marketable yields in all living mulch treatments were always lower than the industry standard. In spite of effective annual grass control, reduced yields may make the adoption of the tested living mulch species less attractive to conventional sweet corn 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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".