Seasonal susceptibility of boreal plants: red raspberry phenology as a bioindicator of optimum within-season timing of glyphosate applications
Bibliographic record
Abstract
In Canada, forest managers operating under public licenses are under pressure from the public to cease using herbicides or at minimum reduce the quantity of active ingredient applied in the environment. Lacking in their decision-making toolbox is information about biological cues that could help optimize herbicide performance. In 1990, two rates of the herbicide glyphosate, 1.1 and 1.7 kg acid equivalent (a.e.) ha -1 , were applied bi-weekly between July 21 and September 25 using a backpack sprayer to release jack pine (Pinus banksiana Lamb.) seedlings from red raspberry (Rubus idaeus L. var. strigosus (Michx.) Maxim.) competition. On average, the higher application rate reduced raspberry cover by at least 6% more than the lower rate (p < 0.01). Control of raspberry was poor with the earliest application, peaked with mid- to late-summer applications, and decreased with late-season applications. Peak jack pine performance, as measured by stem volume index, followed a mid-August application at the low rate. Earlier applications resulted in substantial herbicide injury and later applications were not as effective at reducing raspberry competition. The optimum timing for jack pine performance corresponded with the period between the beginning of raspberry's floricane senescence (i.e., end of full flowering) and the initiation of primocane senescence (i.e., fruit maturation). Seedlings released in mid-August maintained a growth advantage over other seedlings from the fifth through to the tenth year of this study. Discerning forest managers may choose to use phenological cues from the target species, such as red raspberry, as a bioindicator of glyphosate efficacy. Key words: forest vegetation management, herbicides, herbicide efficacy, phenology, Pinus banksiana, Vision ® , glyphosate, Rubus idaeus
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".