Spatio-temporal variability of <i>Nephrops norvegicus</i> density distribution on the Aran grounds and its implications for ecosystem-based fishery management
Bibliographic record
Abstract
Abstract Under the EU biodiversity strategy, changes in fishery management are expected to move fishing activity in Europe towards an ecosystem-based approach by 2030 and to achieve this goal it is essential to identify the spatial distribution of the most valuable commercial fisheries. Using geo-referenced census data of Nephrops norvegicus burrows from underwater TV surveys, the spatio-temporal distribution patterns on the Aran grounds (west of Ireland) from 2002 to 2018 was investigated in relation to habitat and fishing exploitation. A geostatistical approach revealed a patchy distribution, varying in size and intensity over the years. The mud content of the seabed was not influential in explaining spatial variability of burrow distribution. Spatio-temporal analysis showed an overall depletion of burrow abundance over the central area of the study contrasting with its margins and leading to an increase in vessel search activity towards the periphery. This study of the Aran ground stock revealed a decreasing trend in N. norvegicus density from 2002 to 2018, despite increasing landings. It also highlighted spatially variable adverse effects of fishing pressure across different areas. These findings may inform habitat conservation planning in line with EU regulations on fishing impacts.
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 | 0.000 |
| 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".