Bibliographic record
Abstract
The St. Lawrence River, a critical navigational and ecological artery in North America, plays a dual role in supporting both the economy, through the transport of goods and tourism, and societal well-being with leisure activities. The escalating issue of ships operating at increasing speeds has sparked concerns about the potential for accidents, environmental harm, and adverse socio-economic consequences for local communities in Quebec and Canada. This thesis examines the perceptions of local communities regarding the impacts of marine traffic, including recreational and commercial vessels, along the St. Lawrence River. The study encompasses a wide range of community perspectives gathered through a survey conducted from June to November 2023 in communities near the St. Lawrence waterway. The survey, supported by a conceptual framework, investigates various aspects of perception, including interactions with the river, identified areas of concern, and perceived effects of marine traffic on ecological systems, community well-being, and safety. By employing a mixed-methods approach, this analysis incorporates socio-economic, demographic, and geographic contexts, regression models, spatial analysis, and qualitative coding in order to reveal patterns and perceptual themes within the responses. The study reveals that socio-economic and demographic factors, such as age, income, place of residence, and employment sector(s), play a significant role in shaping individuals' perceptions of the effects of maritime traffic. Perception is also influenced by geography, as evidenced by the variation in responses observed in different administrative regions. Ultimately, analysis of the qualitative data showcases distinct patterns, highlighting the amplification of shipping-induced waves caused by high speeds, resulting in coastal erosion and safety concerns arising from dangerous user behavior. The pivotal role of this work lies in its ability to establish a connection between the broader research community, the shipping industry, and the general public, thereby improving our understanding of perceived impacts of maritime traffic. The findings significantly contribute to the on-going discussion on St. Lawrence maritime traffic and provides valuable actionable insight for policy makers
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".