The environmental fate and effects of antifouling paint biocides
Bibliographic record
Abstract
Antifouling (AF) biocides are the active ingredients in AF paints that prevent the settlement, adhesion and growth of organisms to a painted surface. A wide range of chemicals are used as AF biocides, which have very different physico-chemical properties and therefore differing environmental fates, behaviour and effects. Copper has been used as an antifoulant for centuries and extensive research has been performed to understand how copper speciation influences bioavailability and toxicity. For biocides that have been widely used over a number of decades, for example Irgarol 1051 and diuron, there are a large amount of environmental data in the public domain, including for their respective metabolites, that allows their environmental safety and potential risk to the environment to be assessed. For other biocides such as dichlofluanid, DCOIT (SeaNine 211) and zinc/copper pyrithione, there is a good understanding of their fate and effects. However, few monitoring studies have been performed and not so much is known about the fate and effects of their metabolites. There are also new or candidate biocides such as triphenylborane pyridine, Econea, capsaicin and medetomidine for which there is very little information in the public domain. This review provides an overview of the environmental fate and occurrence data that are in the public domain for AF biocides and provides some insight into the effects of these compounds on non-target organisms.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".