Production and export potential of the resource-based industries of Canada in intracontinental conditions
Bibliographic record
Abstract
The transport-geographical and transport-economic continentality of the resource-extraction industries of Canada is considered. A study of the distribution of extraction and export of raw materials according to the zones of remoteness from the sea routes showed that the example of Canada does not fit in with a global pattern. A change in the territorial structure of Canada’s extractive complex most dramatically shows a shift of the resource-producing sectors far inland, whereas the main trend worldwide has been the movement from the landlocked areas to the sea. The intracontinental functioning predetermines an increase in specific transportation costs and enhances the negative influence upon the finance and economic indicators of the operation of producers and exporters of raw materials. For offsetting the costs connected with the intracontinental location of its resource-extraction facilities Canada, first, is using to advantage its border location, and, second, a relatively inexpensive pipeline and railroad transport is being used in transporting raw materials. On the other hand, it is the economic sea transport that serves as the main vehicle in decreasing the transportation costs of Canadian raw materials delivered to the world market. The cost of shipping via sea routes justifies and offsets the transportation costs incurred in the event of using land transport between the place of extraction of raw materials and the ports of exportation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".