Testing a New Anti-Zebra Mussel Coating with a Multi-plate Sampler: Confounding Factors and other Fuzzy Features
Bibliographic record
Abstract
In situ experiments were conducted to assess the use of multi-plate samplers for antifouling experiments and to test the antifouling effectiveness of a chitin-based coating against zebra mussels (Dreissena polymorpha). A series of multi-plate samplers consisting of three parallel plates coated with chitin, and untreated control samplers, were submerged horizontally or vertically in a marina of the St Lawrence River for 3 1/2 months (July-October) in 1998 and 1999. Mussels attached to the chitin-treated substrates were on average 2.75 times more abundant than on the control samplers, indicating that chitin is not effective as an antifouling agent against zebra mussels. The abundance, size distribution and spatial dispersion of mussels on the plates varied both between plates and between top vs bottom sides of plates in horizontal collectors, but not in vertical samplers. The three plates composing each multi-plate horizontal sampler do not represent true replicates for statistical analysis. The bottom side of plates exhibited the least variability and might therefore serve as the experimental unit. Substrate heterogeneity and plate orientation were identified as confounding factors to be controlled for in future experiments. Sunlight exposure and colonization by sponges strongly influenced zebra mussel abundance and should be considered when performing in situ experiments. Because of the influence of uncontrolled factors, it is recommended that in situ pilot studies be conducted to statistically test the effectiveness of antifouling products once the threshold level of the desired effectiveness is defined.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".