Environmental vulnerability profiles: characterization of pressures in the southern Gulf of St Lawrence, Canada
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The worldwide realignment of research and management objectives in recent years to respond to implementation of the “ecosystem approach” represents a departure from past practices where emphasis was mainly on a single-species fisheries or single-activity basis. Although there is a broad consensus on the purpose of this realignment, its practical application continues to be a significant challenge given the complexities of attempting to develop multispecies/activity integrated management plans, frameworks, and approaches for all factors affecting the aquatic environment. The management of human activities, both aquatic and land-based, that contribute to adverse environmental impacts on aquatic ecosystems is one of the major challenges associated with integrated coastal and ocean management. There is recognition that effective integrated management will require new pragmatic approaches, in addition to current practices. It is also particularly important to develop approaches that build greater credibility with the public with regard to integrated planning initiatives and that are based on clear, factual, and interpretable information. The Gulf of St Lawrence Regional Vulnerability profile is being developed by the Department of Fisheries and Oceans Canada with contributions from a number of federal and provincial departments. The purpose of this atlas is to identify and scope the environmental pressures associated with a number of human activities from a geospatial perspective and to illustrate their respective and relative intensity. This is not an assessment, but may serve to identify research or assessment needs. It can also be the basis for engaging relevant federal and provincial partners as well as for communicating with the public in a structured and factual manner.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".