Efficient and rapid control of Eurasian watermilfoil (<i>Myriophyllum spicatum</i>) by combining benthic mats and hand pulling
Bibliographic record
Abstract
Abstract Eurasian watermilfoil (Myriophyllum spicatum L.) is an aquatic vascular plant that forms extensive dense beds in lakes. This invader competes with native plants, interferes with aquatic activities, and decreases riparian property values. In Canada, the use of aquatic herbicides is highly restricted. Environmental managers must therefore rely on physical methods such as hand pulling or benthic matting for control. Although these methods are not new, there has been little scientific investigation regarding their effectiveness and cost over multiple years. Benthic matting and hand pulling were used in Lac des Abénaquis (area: 1.2 km2) to control 3.6 ha of M. spicatum beds. Initiated by citizens in 2016, control procedures were scientifically studied in 2020 and 2021. Benthic fiberglass mats were deployed on dense M. spicatum patches for 10 wk. Isolated plants and patches <100 m2 were hand pulled by divers, and the harvested material surfaced via a suction hose or in hand-filled bags. By August 2021, all the M. spicatum patches had been eliminated, and only 560 widely scattered plants remained. Over the last 2 yr of control, hand pulling required 243 person-hours and removed 2,245 kg of biomass. The biomass brought to the surface was 2.4 times higher per person-hour with the suction system than with bags. The use of 1,000 m2 of benthic mats required 47 to 51 person-hours per summer season, including installation, removal, and maintenance. Intensive management (years 1 to 5) using benthic mats and hand pulling cost an estimated Can$185,000 (US$140,000) ha−1 of M. spicatum bed. Hand pulling of scattered individuals (years 6+), estimated at Can$20,000 (US$15,000) per summer, is essential to avoid reinfestation. An invasion of M. spicatum can successfully be managed in small lakes without herbicides, but control remains a costly and long-term endeavor.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".