Removal of deep-sea sponges by bottom trawling in the Flemish Cap area: conservation, ecology and economic assessment
Bibliographic record
Abstract
Deep-sea sponge grounds are vulnerable marine ecosystems, which through their benthic-pelagic coupling of nutrients, are of functional relevance to the deep-sea realm. The impact of fishing bycatch is here evaluated for the first time at a bathyal, sponge-dominated ecosystem in the high seas managed by the Northwest Atlantic Fisheries Organization. Sponge biomass surfaces created from research survey data using both random forest modeling and a gridded surface revealed 231,140 t of sponges in the area. About 65% of that biomass was protected by current fisheries closures. However, projections of trawling tracks estimated that the sponge biomass within them would be wiped out in just 1 year by the current level of fishing activity if directed on the sponges. Because these sponges filter 56,143 ± 15,047 million litres of seawater daily, consume 63.11 ± 11.83 t of organic carbon through respiration, and affect the turnover of several nitrogen nutrients, their removal would likely affect the delicate ecological equilibrium of the deep-sea benthic ecosystem. We estimated that, on Flemish Cap, the economic value associated with seawater filtration by the sponges is nearly double the market value of the fish catch. Hence, fishery closures are essential to reach sponge conservation goals as economic drivers cannot be relied upon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".