Effects of milfoil weevils and weather on the control of Eurasian watermilfoil
Bibliographic record
Abstract
We examined the response of Eurasian watermilfoil (Myriophyllum spicatum) coverage on Manistee Lake, Michigan (U.S.A.) in the presence of milfoil weevils (Euhrychiopsis lecontei). Among 150 sites, milfoil presence declined from 2008 levels of 34 (23%) sites to 2 (1%) sites by 2015 coincident with cumulative stocking of 259,500 weevils from 2007 to 2014. Severe winter temperatures also were associated with milfoil declines. Each 1°C decline in average low temperature during the preceding winter was associated with 3.4 (95% CI 0.8–6.1) fewer sites with milfoil. Impacts of weevil herbivory on watermilfoil may be accentuated by severe winter temperatures. Lake managers should, when possible, integrate weather conditions with weevil stocking regimes to control Eurasian watermilfoil.
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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 | 0.001 |
| 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) | 0.001 | 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".