Bibliographic record
Abstract
Toronto Union Station Rail Corridor which currently contains 35 miles of track, 142 turnouts, 250 switch machines, and 234 signals, combining to yield 4,000 possible routes with a superbly maintained 1928 electro-mechanical interlocker will soon undergo a transformation from electro-mechanical to microprocessor based interlocking control. The replacement will be a state of the art system that will be part of a C$250 million resignaling program to make Toronto Union Station the most operationally advanced passenger terminal on the continent and double its existing capacity when completed. It typifies what interlockings can do, and are doing, for today's railroads and transit systems. Since the bidding has been opened to both North American and European architecture, the project could present a real watershed in interlocking technology. The project involves every part of the infrastructure, trackwork, communications, electrical systems, and passenger areas, and the management consortium's objective is to set out requirements and let the bidder prescribe solutions. That means that both North American and European interlocking architecture are relevant if they can meet GO Transit's needs.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.074 | 0.017 |
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".