Mini-review: Impact and dynamics of surface fouling by solitary and compound ascidians
Bibliographic record
Abstract
Globally, ascidians are a significant contributor to benthic marine fouling communities, but have remained poorly studied in this context. In some cases, such as in shellfish and finfish aquaculture, ascidians are the most problematic of all fouling organisms. The disproportionate impact of ascidian fouling in some specific geographic locations has been related directly to anthropogenic translocation of these organisms around the globe. In the case of ascidians, therefore, the economic issue of biofouling and the ecological ramifications of invasion are inextricably linked. This mini-review briefly discusses the introduction of non-native ascidians to areas where they have subsequently proven to be a significant fouling pest. The elements of ascidian reproductive ecology that support their aggressive fouling character are discussed and the scant information pertaining to their adhesion and adhesives is presented. Finally, strategies for mitigating ascidian fouling are examined. It is suggested that sufficient working knowledge currently exists to support the inclusion of one or more common ascidian species as 'standard' test organisms used for evaluation of novel fouling-resistant surfaces.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".