Process-driven and biological characterisation and mapping of seabed habitats sensitive to trawling
Bibliographic record
Abstract
The increase of anthropogenic pressures on the marine environment together with the necessity of a sustainable management of marine living resources have underlined the need to map and model coastal environments, particularly for the purposes of spatial planning and for the implementation of integrated ecosystem-based management approach. The present study compares outputs of a process-driven benthic habitat sensitivity (PDS) model to the structure, composition and distribution of benthic invertebrates in the Eastern English Channel and southern part of the North Sea. Trawl disturbance indicators (TDI) computed from species biological traits and benthic community composition were produced from samples collected with a bottom trawl. The TDI was found to be highly correlated to the PDS further validating the latter's purpose to identify natural process-driven pattern of sensitivity. PDS was found to reflect an environmental potential that may no longer be fully observable in the field and difference with in situ biological observations could be partially explained by the spatial distribution of fishery pressure on the seafloor. The management implication of these findings are discussed and we suggest that, used in conjunction with TDI approaches, PDS may help monitor management effort by evaluating the difference between the current state and the presumed optimal environmental status of marine benthic habitats.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".