Bibliographic record
Abstract
GO Transit is a combined regional network of trains and buses that serves the Greater Toronto and Hamilton Area (GHTA). Train ridership on the network is expected to nearly double by 2020. In order to remain competitive with frequent and reliable service, GO Transit has embarked on the Georgetown South Project (GTS), which is all about capacity. The objective of the GTS Project is to meet the needs of GO Transit's Kitchener, Milton and Barrie rail corridors, while building infrastructure to meet future needs. The GTS Project consists of the widening and modifying of 15 bridges; the construction of one new bridge; seven road-to-rail grade separations; one rail-to-rail grade separation; lowering the rail corridor at three locations; station work and major track and grading construction; civil works; signal installations; and utility relocation. In addition to this work the GTS Project rail expansion will accommodate the new Union Pearson Express that will connect Toronto Pearson International Airport and Union Station. Prior to the start of the project, GO train service in the region was limited only to rush-hour because of the availability of a single track north and west. Once the project is complete, the expanded corridor will accommodate between four and eight total tracks. If the improvements to the corridor are not made, the region will require eight additional highway lanes by 2031 in order to meet the demand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.010 |
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".