The effects of hexazinone rates, application timing, and residue management on Canada thistle control and alfalfa seed production
Bibliographic record
Abstract
Hexazinone is an effective weed control tool in alfalfa seed production. However, both researchers and producers have had variable success in Canada thistle control with hexazinone. The objective of this study was to determine the effects of hexazinone rates, application timing and residue management on Canada thistle control and alfalfa seed production. Two field trials were established with ‘Algonquin' alfalfa near Valparaiso, SK and Carrot River, SK in 1998. The Valparaiso trial was located on a fine-textured soil, high in soil organic matter. The Carrot River trial was located on a coarse-textured soil, low in soil organic matter. Three factors (three rates of hexazinone, three residue management treatments and two application dates) were tested in a randomized complete block design. Alfalfa seed yield and Canada thistle: density, dry matter and seed contamination, were determined in 1999. Alfalfa seed yield and Canada thistle control (density, dry matter and seed contamination) increased with increased rates of hexazinone at Carrot River but not at Valparaiso. No significant interactions between hexazinone rates, application timing and residue management for Canada thistle control were observed. However, residue management by application timing interaction was significant for alfalfa seed yield. Alfalfa seed yield was significantly higher following a spring burn at both sites.
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 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".