Identifying potentially harmful jellyfish blooms using shoreline surveys
Bibliographic record
Abstract
Interactions between jellyfish and aquaculture operations are frequent around the world, with scyphozoan (in particular Pelagia noctiluca) and hydrozoan species documented as causative agents in major fish kills. Identifying areas of major aggregations or incursions of particular species around a coastline is a good starting point when assessing the threat of jellyfish blooms to existing or potential aquaculture facilities. Here we tested the viability of shoreline surveys to identify areas at risk from coastal and/or oceanic jellyfish species. Surveys were undertaken at over 40 sites around the north of Ireland (covering ~1800 km of coastline) from 2009 to 2011 to test 2 specific hypotheses: (1) strandings of coastal jellyfish species with life cycles involving production of medusae from benthic polyps or hydroids would display a marked spatial consistency over time, although the magnitude of events may vary inter-annually; and (2) incursions of oceanic jellyfish species (lacking polyps) would impact large areas of coastline and be more episodic in nature. Seven jellyfish species known to harm farmed finfish displayed spatially consistent stranding distributions, with major stranding events evident at several locations. More generally, coastal species stranded throughout the study area at the end of summer, whilst oceanic species were found along the exposed north shore of Ireland, washing ashore during the autumn/winter. The numbers of individuals within stranding events were greater for oceanic species (e.g. P. noctiluca, mean ± SE = 1801 ± 978 ind. km-1) than coastal species (e.g. Aurelia aurita = 112 ± 51 ind. km-1), supporting the idea that large offshore aggregations of P. noctiluca remain a threat to the aquaculture industry across the region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".