Evaluation of a new biological control pathogen for Management of Eurasian Watermilfoil
Bibliographic record
Abstract
Abstract : This technical note describes the results of an aquarium study to evaluate the effectiveness of a potential fungal pathogen in managing the nuisance submersed plant Eurasian watermilfoil Myriophyllum spicatum L. (Eurasian watermilfoil; hereafter called milfoil) was first documented in the United States in 1942 but its introduction could have taken place much earlier (Couch and Nelson 1985). It now occurs in lakes, ponds, reservoirs, or rivers in 48 states (excluding Wyoming and Hawaii) and in the Canadian provinces of British Columbia, Ontario, and Quebec. Herbarium records indicate that there could have been multiple introductions, as early reports came from widely separated locations including Washington DC, the Midwest, and Arizona and California (Smith and Barko 1990). Milfoil spreads naturally by fragmentation and stolons, and anthropogenically on boating equipment. Like other aggressive invasive species, milfoil displaces native species, thereby reducing biodiversity. Its ability to grow at low temperatures allows it to quickly reach the water surface, forming a canopy that shades out other aquatic vegetation (Madsen et al. 1991). Excessive growth adversely affects recreational activities such as swimming, boating, and fishing and degrades the aesthetic appeal of a water body. Additionally, excessive growth results in clogged intakes of industrial and power-generating facilities, lowered dissolved oxygen, and increased mosquito breeding sites (Bates et al. 1985). Traditionally milfoil has been controlled with mechanical removal or herbicide applications. According to Sorsa et al. (1988), the former is cost prohibitive and the latter potentially controversial due to real or perceived threats to human health and the environment. Biological control has been studied as an option for milfoil management for over 40 years.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".