Bibliographic record
Abstract
The United States is currently a net importer of canola (Brassica napus L.) and, to\nbecome more self-sufficient in production of the commodity, the USDA has prioritized research\nthat will allow expansion of canola production into new areas. Canola offers a possible solution\nfor agricultural producers in Texas and the broader southern region looking for a winter\nrotational crop for traditionally limited cropping rotations, but the lack of research and data on\nagronomic management practices specific to the region is a roadblock to adoption.\nThe first objective of this project was to identify the optimum row spacing and planting\ndensity to achieve maximum yield and oil productivity in fall-planted spring canola in the\nsouthern US. Replicated studies were carried out at College Station and Perry, TX during the\n2017-2018 winter growing season. Treatments included three row spacings (19, 38, and 76 cm),\nthree planting rates (1.7, 3.4, and 5.0 kg ha^-1), and two canola cultivars (cv. ‘HyCLASS 930’ and\ncv. ‘HyCLASS 970’). A 15% reduction in yield was observed at the wide 76 cm row spacing at\nPerry, showing risk in planting on rows this wide. The lack of differences in yield among the\ntested planting rates suggests that rates can be dropped as low as 1.7 kg ha^-1 in this environment,\nfar lower than the commonly recommended 5.6 kg ha^-1. The average yield at Perry (2787 kg ha^-1)\nwas comparable to the average 2017 yield in Canada (2300 kg ha^-1), indicating great potential for\nfall-sown spring canola production in Texas.\nThe second objective was to assess potential variety-specific residual chemical effects of\nwheat chaff on canola germination and early growth in laboratory and outdoor pot studies. In the\nlaboratory study, designed to test the most severe possible effects, canola germination and\nradicle elongation rates were measured with exposure to aqueous wheat chaff extract solutions at\nsix concentrations (0, 5, 25, 50, 75, and 100 g L^-1) in petri dishes. Increasing chaff concentration\ninitially slowed germination, but no differences in germination percentage were observed after\nfour days. Persistent negative effects on radicle growth were observed, as radicle length was 45%\nlower with exposure to 100 g chaff L^-1 after four days. In a pot study repeated with chaff from\ntwo sources, experimental treatments included two soil types, chaff of 15 wheat cultivars, and\nuntreated controls. Pots were topped with chaff, placed outside for the summer, and planted with\ncanola in the fall. Wheat chaff did not affect germination, but early growth increased by an\naverage of 23% in 13 of 15 chaff varieties. These results indicate that chemical properties of\nwheat chaff can negatively affect canola seedlings, but these negative effects are unlikely under\nfield conditions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".