Bibliographic record
Abstract
With almost 100 million tons of cargo yearly, of which more than 80 millions pass through Québec ports, the St. Lawrence is an important artery of transport. The fluvial communications System, comprising gulf, estuary, river, and the Great Lakes, extends deep into the north American continent; Duluth, for example, is 2,200 miles from the sea. Although the Seaway has opened the Great Lakes to ocean-going ships, the general increase in tonnage and draught of these vessels has allowed Québec ports, located at the break point between maritimes and Great Lakes traffic, to maintain their privileged position. Nevertheless, transhipment cargoes here are not particularly impressive (about 30 million tons yearly, mainly cereals) if one considers the enormous industrial capacity of the basin; this is because the Great Lakes-St. Lawrence network is in many ways an autonomous unit, essentially continental, and supplied from the interior rather than from the exterior, as is the case of the Rhine basin, for example. In Québec, cargoes originating from « secondary » industries scattered along the river and its tributaries (for example, the Saguenay) are relatively unimportant in terms of tonnage ... only 22 million tons of cargo from these industrial zones pass through Québec ports. Only 3.9% 0of the 6,539 factories located in cities touching on water, and only 13% of the total industrial payroll in these cities, are directly related to the St. Lawrence. This « underuse »of the river can be explained by Québec s industrial structure, geared to the production of consumer goods for local markets ; in contrast, Ontario has much heavy industry tied directly to the St. Lawrence network.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.196 | 0.084 |
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".