Hot Spots for Vessel-to-Vessel and Vessel-to-Fixed-Object Accidents Along the Great Lakes Seaway
Bibliographic record
Abstract
In the past decade, an average of 20.5 vessel accidents per year have been reported along the Great Lakes Seaway (GLS) in Canada and the United States, with the vast majority clustered at specific unsafe locations or sites along the route. Sites with an unacceptably high potential for accidents are referred to as hot spots, and these hot spots are prime candidates for safety intervention. Because of the random nature of accidents, the identification of hot spots must be based on robust site-specific prediction models of accident expectation. This paper presents an empirical Bayes prediction model developed for the GLS that considers four types of accident scenarios: vessel to vessel and vessel to fixed objects, for river and for canal or lock sections. Hot spot sites are determined with two risk tolerance thresholds: 95th percentile exceedance (high-risk sites) and 85th percentile exceedance (moderate-risk and high-risk sites). For the 95th percentile threshold and vessel-to-vessel accidents, five hot spots were identified on the 1,600-km length of the GLS studied (excluding lake or port areas). Of the designated hot spot sections, 10 km (60.6%) were located along natural river courses and the rest at canals or locks. For vessel-to-fixed-objects accidents, all high-risk hot spots were at canal or lock sections (15.5 km). Reducing the threshold to the 85th percentile resulted in a 7.8% increase in seaway length that was designated as a hot spot. The locations of these hot spot sections along the GLS were consistent for both thresholds.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".