Intermodal freight terminals: locality and industrial linkages
Bibliographic record
Abstract
The areas around eight Canadian intermodal freight terminals form the focus of this study. Two basic research questions are addressed: What is the character of the zones adjacent to the terminals and what is the functional tie between industries located in these zones and the terminals themselves? There are three seaports (Halifax, Montreal and Vancouver), three airports (Dorval‐Montreal, Pearson‐Toronto and Vancouver) and two rail yards (both in the Toronto region) in the study. In total, 196 manufacturing and wholesaling firms were part of the study. Transportation land use is areally most extensive in six of the eight terminal zones. Industrial land use, while significant in area, is not the most dominant land use surrounding any of the terminals. No one socio‐economic characteristic defines the areas around the terminals. Businesses in close proximity to the terminals make rather modest use of the terminals. Less the 30 percent of the interviewed firms used the nearby terminal for their freight shipments; only 3 percent of the firms indicated that proximity to the terminal was a primary locational consideration. The relationship between industrial location and the terminals is more indirect, than direct, based on the high level of accessibility found in the terminal zones.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".