Comparison of four glyphosate herbicide formulations for white spruce release treatment
Bibliographic record
Abstract
An operational field trial was established to compare the efficacy and white spruce (Picea glauca (Moench) Voss.) tolerance of three alternative glyphosate formulations to Vision®, Canada's originally registered forestry formulation. The trial was done at operational scale (minimum treatment unit 7 ha) using helicopter application of all products. The tested alternatives included two new generic formulations, recently registered for forestry in Canada (Glyfos-Forza® and Vantage®), and one widely used formulation in the United States (Accord®). All four formulations provided greater than 90% control of bluejoint reedgrass (Calamagrostis canadensis (Michx.) Beauv.) through 23 months post-treatment (p > 0.27). Assessed over the same period, the three Canadian formulations provided equivalent white spruce tolerance (p > 0.10), with seedlings exhibiting only minor, non-lethal herbicide injury. Accord®, not registered for use in Canada, consistently provided the best crop tolerance, but differences were only statistically significant 11 months post-treatment (p = 0.10). The results suggest that foresters may choose among the glyphosate formulations available in Canada without concern for product-related differences in efficacy and white spruce tolerance. Key words: glyphosate, white spruce, conifer tolerance, Calamagrostis canadensis, efficacy
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".