North Atlantic basin-scale multi-criteria assessment database to inform management recommendations to protect Vulnerable Marine Ecosystems
Bibliographic record
Abstract
We applied the International Council for the Exploration of the Sea (ICES) multi criteria assessment (MCA) method for identifying VMEs in the North-East Atlantic (ICES, 2016a,b; Morato et al., 2018) from ATLAS VME database to provide the first North Atlantic Ocean basin-scale VME assessment. This MCA is a taxa-dependent spatial method that incorporates the fact that not all VME indicators have the same vulnerability to human impacts, and thus should not be weighted equally. By including a measure of the confidence associated with each VME record, this methodology also considers some of the uncertainties associated with the sampling methodologies, the reported taxonomy, and data quality issues. Equally important, this dataset highlights areas in the North Atlantic that have been poorly sampled and that require further attention. Finally, this methodology also allows for the evaluation and comparison of the VME likelihood with spatial fisheries data that may directly generate significant adverse impacts on VMEs. In the data report, we made the “North Atlantic basin-scale VME index dataset” publicly, thus allowing its consultation and use by scientists, managers, or other relevant stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.178 | 0.001 |
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; both teacher heads agree on what is shown here.
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".