Field report passive fishing in offshore wind farm Borssele
Bibliographic record
Abstract
This project developed a research outline together with commercial, small-scale fishers to test fishing methods that could be suitable for fishing in offshore wind farms, including their economic viability, ecological effects, and safety requirements.This is done so that (experimental) passive fishing in wind farms can be initiated.This was done in close collaboration with the fishers in a focus group.Previous to this study, a desk study was performed to collect all the knowledge and information present concerning passive fishing in relation to offshore wind (Neitzel et al. 2023).This knowledge was used to design the field tests for this study.This research investigates further possibilities of commercial passive fishing techniques when looking at operational factors, safety, fishing gear aspects, economic feasibility and ecology.This report describes what has been done in the practical (fieldwork) part of this study and gives a first impression of the lessons learned from the field.The data collected will be further analysed and presented in the final report that will be delivered at a later stage of this project.The operations took place in Borssele I and II offshore wind farm off the Dutch coast during the period April to October 2023.During the field experiments, a total of 35 days were conducted in the Borssele I and II offshore wind farm.The field experiments carried out in the project were as follows: 5 days using gill nets with YE152. 16 days using 4 different types of pots with YE152. 4 days using handlines with KG7. 10 days using a mechanical jigging system with MDV2.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".