Development of marine antifouling performance in hard fouling-release coatings
Bibliographic record
Abstract
Marine biofouling is a substantial economic and environmental issue. Hard fouling-release coatings present a promising solution, combining fouling-release characteristics with durability. This study tested proprietary hard fouling-release prototype coatings from GIT Coatings, Inc. alongside uncoated controls, colour controls, and commercial performance standards. Three successive experiments were completed, incorporating static and dynamic flow conditions at sites in Nova Scotia, Canada. Initially, biofouling percent cover and cleanability for prototype coatings were comparable to untreated controls. By the final experiment, prototype coatings had significantly lower percent covers than both uncoated controls and the durability performance comparison, Ecospeed. Furthermore, several prototype hard fouling-release coatings had comparable percent cover (and possibly cleanability) to the fouling-release performance comparison, Intersleek. The results indicate that hard fouling-release coatings with potentially greater durability and longevity can achieve similar fouling-release performance as commercial fouling-release coatings. Further tests are needed to determine if unintended toxicity contributes to the antifouling effects.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".