Efficient and rapid control of Eurasian watermilfoil (<i>Myriophyllum spicatum</i>) by combining benthic mats and hand pulling
Why this work is in the frame
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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 km 2 ) 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 m 2 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 m 2 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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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 | 0.001 | 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 | 0.001 |
| 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 it