Canada thistle (<i>Cirsium arvense</i>) management in canola (<i>Brassica rapa</i>) and barley (<i>Hordem vulgare</i>) rotations under zero tillage
Bibliographic record
Abstract
The effect of in-crop herbicide rate, crop row spacing and seeding rate on Canada thistle [Cirsium arvense (L.) Scop.] management in two cycles of a canola (Brassica rapa L.)/barley (Hordeum vulgare L.) rotation was investigated under zero tillage at Vegreville, Alberta. The entire plot area received pre-harvest glyphosate from 1993 through 1995. In crop, either no herbicides were applied or clopyralid and dicamba/MCPA-K were applied at one-half or full recommended rates to canola and barley, respectively. In most cases, Canada thistle shoot density and dry weight were lower when the herbicides were used at either rate compared with no herbicide application. Pre-harvest glyphosate followed by either clopyralid or dicamba/MCPA-K in-crop reduced Canada thistle shoot densities from approximately 20 m–2 in 1993 to one or fewer m–2 in 1996. In-crop herbicides resulted in higher crop yields and revenues in 1993 and 1994, but not in 1996 when the Canada thistle infestation was relatively low. The effect of crop row spacing was inconsistent, and had little effect on Canada thistle shoot density or dry weight. In some cases, crop yield was higher at 20-cm than at 30-cm row spacing. Crop seeding rate had no effect on crop or Canada thistle variables. Key words: Cirsium arvense, zero tillage, pre-harvest glyphosate, clopyralid, dicamba/MCPA-K, crop row spacing, crop seeding rate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".