Characterizing Exposure to and Sharing Knowledge of Drivers of Environmental Change in the St. Lawrence System in Canada
Bibliographic record
Abstract
The St. Lawrence is a vast and complex socio-ecological system providing a wealth of services sustaining numerous economic sectors. These ecosystems are subject to significant human pressures that overlap and potentially interact with climate driven environmental changes. Our objective in this paper is to systematically characterize the distribution and intensity of drivers in the St. Lawrence System. To do so, we launch eDrivers, an open knowledge platform gathering experts committed to structuring, standardizing and sharing knowledge on drivers in support of science and management. We gathered data on 22 coastal, climate, fisheries and marine traffic drivers through collaborations, existing environmental initiatives and open data portals. We show that few areas of the St. Lawrence are free of cumulative exposure. The Estuary, the Anticosti Gyre and coastal areas are particularly exposed, especially in the vicinity of urban centers. We identified 6 areas of distinct cumulative exposure regime that show that certain drivers typically co-occur in different regions of the St. Lawrence and that coastal areas are exposed to all driver types. Of particular concern are two threat complexes capturing most exposure hotspots that show the convergence of contrasting exposure regimes at the head of the Laurentian Channel. eDrivers was built on a series of guiding principles upholding existing data management and open science standards. We therefore expect it to evolve through time to address knowledge gaps and refine current driver layers. Ultimately, we believe that eDrivers represents a much needed solution that could radically influence broad scale research and management practices by increasing knowledge accessibility and interoperability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".