Watercraft Decontamination Practices to Reduce the Viability of Aquatic Invasive Species Implicated in Overland Transport.
Bibliographic record
Abstract
Abstract Recreational boating activities enable aquatic invasive species (AIS) dispersal among disconnected lakes, as invertebrates and plants caught on or contained within watercraft and equipment used in invaded waterbodies can survive overland transport. Resource management agencies worldwide recommend decontaminating watercraft and equipment using high water pressure, rinsing with hot water, or air-drying for up to seven days to inhibit this mode of secondary spread. There is a lack of studies on the efficacy of these methods under realistic conditions and considering feasibility for recreational boaters. Hence, we conducted experiments addressing this knowledge gap using AIS present in Ontario, namely zebra mussels (Dreissena polymorpha), banded mystery snails (Viviparus georgianus), spiny waterfleas (Bythotrephes cederstroemi), Eurasian watermilfoil (Myriophyllum spicatum), Carolina fanwort (Cabomba caroliniana), and European frogbit (Hydrocharis morsus-ranae). Washing at high pressures of 900-1200 psi removed the most biological material (90%) from surfaces. Brief (<10s) exposure to water at ≥60°C caused nearly 100% mortality among all species tested, except snails. Acclimation to temperatures from 15°C to 30°C before hot water exposure had little effect on the minimum temperature required for no survival. Air-drying durations producing complete mortality were ≥60h for zebra mussels and spiny waterfleas, and ≥6 days among plants, whereas survival remained high among snails after a week of air-drying. Hot water exposure followed by air-drying was more effective than either method separately against all species tested, reducing either the minimum water temperature or air-drying duration necessary. These findings can inform best management strategies against AIS spread.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".