Bibliographic record
Abstract
This article describes how major investments are helping to turn a once disjointed and run-down collection of lines into a coherent orbital railway network linking London's inner suburbs. The author looks at how the Overground has quickly become a vital part of the city's thriving public transport system. A quarter of a century ago, the North London Line (NLL) was a railway in the throes of seemingly-terminal decline. Despite connecting seven London boroughs, the peak service was just three trains per hour, and three-car emus had been replaced by two-car trains. However, as the 1980s progressed the threat of closure receded and under the renewed political focus of the former Greater London Council the line clung on. Nonetheless, investment was sparse and the line remained something of a backwater until November 2007, when Transport for London's (TfL) rail division, London Rail, took responsibility for five inner-suburban passenger services in the city and pulled them together under the London Overground brand. The result has been transformational. Stations have been refurbished and all are staffed from the departure of the first train to the arrival of the last. The Oyster smartcard was extended to cover the whole Overground network (and recently, other National Rail services in London). Peak frequencies have been stepped up to six trains per hour, and operating hours extended to reflect those of the Underground. The article describes ow passengers have seen good steady progress over the last two years despite the huge amount of work that has been going on behind the scenes. The public now perceives Overground as a metro service, so frequency and reliability have to reflect these expectations. One of the most palpable aspects of the Overground, regeneration is the fleet of 57 class 378 Electrostar emus being supplied by Bombardier. Thirty-four of these trains will be delivered in dual-voltage configuration for the NLL and West London Line (WLL), initially as three-car units with additional vehicles that will allow them to be strengthened to four cars, while platforms are being lengthened to accommodate them.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.434 | 0.281 |
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".