Bibliographic record
Abstract
The St. Lawrence Seaway, which begins at the St. Lambert lock in Montreal and extends 423 miles to Long Point, Canada, on Lake Erie, poses unique challenges to the efficient operation of a vessel traffic control system. More than 4,000 lake and ocean-going vessels, primarily bulk carriers carrying over 40 million tons of cargo, transit the Seaway during the navigation season from late March to late December, transiting 15 locks and crossing the international boundary between Canada and the United States 27 times on their journey. In order to provide a seamless journey, the U.S. St. Lawrence Seaway Development Corporation (SLSDC) and its Canadian counterpart, the St. Lawrence Seaway Management Corporation, jointly operate an integrated traffic management system, featuring 2 vessel traffic control centers in Canada and 1 in the United States. In 2002, the St Lawrence Seaway became the first inland waterway in North America to integrate a new waterways management tool, the automatic identification system (AIS), into its vessel traffic management system. September 2007 marks 5 years since the Seaway fully implemented AIS technology as part of its vessel traffic control system. This article discusses the challenges to implementation of AIS in the Seaway, the substantial benefits AIS has brought, and the overall successful development and utilization of AIS into the St. Lawrence Seaway traffic management system by the SLSDC and its Canadian counterpart.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.027 |
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".