Preliminary results on seabed litter distribution on Flemish Cap (Div. 3M), Flemish Pass (Div. 3L) and Grand Banks of Newfoundland (Divs. 3NO).
Bibliographic record
Abstract
We analyzed seabed litter densities in the NAFO Regulatory Area (NRA; Divs. 3LMNO) using six years of demersal trawling data from the EU-Spain/Portugal groundfish surveys (period 2018–2023). This study provides a preliminary updated information and a baseline information on seabed litter for Div. 3L and Divs. 3MNO, respectively. A total of 1936 valid bottom trawl hauls were analysed (40- 1481 m depth). Litter was found in 16.7% of the valid hauls, with mean densities of 6.7±18.5 items km–2 and 7.7±121.5 kg km-2. Fisheries was found to be the main source of seabed litter, and 41.8% of the hauls with litter presence showed litter included in the fisheries-related litter group category. Whereas in most cases the fisheries-related litter was composed of small fragments of rope, in other cases it was composed of entire fishing gears (e.g., pots from fisheries not managed by NAFO). Plastic, metal and other anthropogenic litter were the next most abundant group categories, accounting for 63.6%, 12.9% and 8.3% of the total seabed litter items recorded, respectively. The results from this study will provide information on the distribution of seabed litter in Divs. 3LMNO and will help to improve the current protocol for collecting seabed litter data and to implement best practices in groundfish surveys conducted in 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".