Mobile bottom fishing in the Canadian Pacific and Atlantic causes disturbance and risk to remineralisation of seabed sediment carbon stocks
Bibliographic record
Abstract
Mobile bottom fishing causes substantial disturbances to seabed sediments–one of the world’s largest organic carbon stores. We estimate that 2.1 and 32.0 Mt of carbon is disturbed by annual fishing activities in the Canadian Pacific and Atlantic, respectively. A net increase in carbon remineralisation from this disturbance could negatively impact the oceanic sink for CO2. Due to high uncertainty in estimating the scale of remineralisation, we construct a semiquantitative measure of relative carbon risk to describe potential differences between locations and fisheries. In the Pacific, shrimp trawling caused the largest total carbon disturbance and risk; groundfish trawling had large total disturbance but lowest mean disturbance and risk per unit effort (PUE); scallop dredging had the smallest total impacts but highest disturbance and risk PUE. In the Atlantic, shrimp trawling dominated total impacts; however, mean disturbance PUE was highest for scallop and clam dredging, and mean risk PUE was highest for groundfish trawling. High spatial variation in these results would allow a targeted management approach. While uncertainties remain, precautionary risk-based ecosystem management should be implemented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".