www.mdpi.com/journal/ijms Biofouling Growth in Cold Estuarine Waters and Evaluation of Some Chitosan and Copper Anti-Fouling Paints
Bibliographic record
Abstract
Abstract: Ecological concerns about antifouling paints containing non-green tin and copper compounds have highlighted the need for environmentally friendly alternatives. We report here a field test conducted in estuarine waters over two months designed to evaluate the efficiency of a number of active natural and man-made chemical ingredients added into a silicon-polyurethane marine pa int. Early steps of biofouling in cold seawater of th e St. Lawrence Estuary (Canada) were observed. Analyses, including dry biom ass, flow cytometry and spectrofluorim etry, dem onstrated a short-term antibacterial actio n of chitosan-based paints although no significant anti-algal action was observed. Cuprous oxide paints were efficient against bacteria and algae invasion in the f irst two w eeks, especially those with added organic biocides such as isothiazolone and copper pyrithione. However, the overall dry biomass and chlorophyll a content were similar for all chitosanand copper-based paints after 63 days. Microsc opic observations reveal ed variation in the highly diverse benthic diatom population including species Navicula, Melosira, Cocconeis, Nitshzcia, Fragilaria and Amphora. Results suggest no real long-term efficiency for tested antifouling paints and highlight a particular need for green an tifouling ingredients that are active under northern estuarine conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".