Bibliographic record
Abstract
the Ontario Commercial Vehicle Survey Abstract: Transshipment has big implications for the provision of public infrastructure and the movements of goods from their point of origin to their final destination. The Ontario Commercial Vehicle Survey proves to be a database that contains substantial transshipment information applicable, indirectly, to goods movement in the United States. The analysis of the Ontario CVS first focused on commodities and their origin/destination facilities, defining terminals and warehouses as possible transshipment locations. Analysis revealed that any commodity is likely to be transshipped through either a truck terminal or a warehouse. A total of six tour structures were seen in the data. All commodity trips can have two or more segments, but most likely trips would involve three legs with two possible transshipment locations. Based on commodity/trip origin and destination coordinates it was possible to determine the distance traveled by each segment of the different tour structures. It was found that the first transshipment location or the first consumer is most likely within a short distance of the shipment’s true origin. That is, the producer (P) and the first transshipment location (W or T) or the first consumer (C) are in the same municipality. Probability distributions were determined for both shipment size and truck type showing that it is very likely a shipment would be at least 1,000 lb and truck type would be a tractor & 1 trailer.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.024 |
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".