The journey of hull-fouling mobile invaders: basibionts and boldness mediate dislodgement risk during transit
Bibliographic record
Abstract
Vessel hull-fouling is responsible for most bioinvasion events in the marine environment, yet it lacks regulation in most countries. Although experts advocate a preventative approach, research efforts on pre-arrival processes are limited. The performance of mobile epifauna during vessel transport was evaluated via laboratory simulations, using the well-known invasive Japanese skeleton shrimp (Caprella mutica), and its native congener C. laeviuscula as case study. The invader did not possess any advantage in terms of inherent resistance to drag. Instead, its performance was conditioned by the complexity of secondary substrate. Dislodgement risk was significantly reduced when sessile fouling basibionts were added, which provided refugia and boosted the probability of C. mutica remaining attached from 7 to 65% in flow exposure trials. Interestingly, the invader exhibited significantly higher exploratory tendency and motility than its native congener at zero-flow conditions. Implications in terms of en-route survivorship, invasion success and macrofouling management are discussed.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
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".