Weed survey of lowbush blueberry fields in Saguenay–Lac-Saint-Jean, Québec, following eight years of herbicide application
Bibliographic record
Abstract
A weed survey was conducted in 1995 and 1996 in eight lowbush blueberry (Vaccinium angustifolium Ait.) producer sites of the Saguenay–Lac-Saint-Jean region to measure the impact of repeated herbicide applications on weed frequency and percent cover. A previous survey (1981–1983), done in the same area before the widespread use of herbicides by the producers, had shown an impact of weed abundance on blueberry productivity. Weed frequency decreased from 1981–1983 to 1995–1996. Some species have apparently disappeared and all showed a reduction in abundance, suggesting that tolerance to herbicides or shifts to herbicide tolerant species has not occurred in this region. Weed cover decreased from over 65% to 10% in surveyed blueberry producer sites, and their presence no longer affects blueberry productivity. However, herbicide use is associated with an increase in percent bare ground, which now accounts for an average of 38% of lot surfaces. Blueberry plants do not seem to be able to quickly colonise bare weeded sites. In plots weeded manually, blueberry shoots did not increase in cover after 2 yr. Herbicide applications are still needed to maintain the weed populations at low levels, but we now have to stimulate the colonisation of bare patches by blueberry plants. Key words: Vaccinium angustifolium, glyphosate, hexazinone, weed control, weed frequency, blueberry productivity
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".