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Record W7061506159

Preliminary results on seabed litter distribution on Flemish Cap (Div. 3M), Flemish Pass (Div. 3L) and Grand Banks of Newfoundland (Divs. 3NO).

2024· article· en· W7061506159 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDIGITAL.CSIC (Spanish National Research Council (CSIC)) · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Generation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsGroundfishLitterSeabedTrawlingDemersal zoneMarine debrisBycatchFishing
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.305
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it