Review of Passenger Railroad EMU and MU Rolling Stock in the US and Canada – Part 2, Connecticut and New Jersey Regions
Bibliographic record
Abstract
Abstract This paper is the second in a series that presents a technical review of the electric, self-propelled, multiple unit railroad rolling stock that were and / or currently are in service in the United States and Canada. The goals of this paper series and therefore the abstract from the first paper remain unchanged. As such, portions of the abstract are repeated for the convenience of the reader. Since the invention of the first electrified, self-propelled rail vehicles by Siemens & Halske in 1879 as well as the advancements of Frank Sprague from 1886, self-propelled, electric traction rail vehicles have evolved into an amazing variety of use cases, shapes and sizes to the present date. With the amelioration of each generation, the electrical and mechanical engineering disciplines have developed a high degree of cooperation and integration to what has evolved into a seamless systems approach that allows agencies and railroads to enjoy record breaking, yet safe commuter and short-haul passenger railroad service with an array of amenities and technical advancements. The core of these rail vehicles are and were humble looking Electric Multiple Unit (EMU) and Multiple Unit (MU) trains that unceremoniously ply the rails around major cities with hundreds of daily riders on board. These otherwise non-descript vehicles often have mundane identifications such as “MP-54” or “M9.” Once in a while, one of these workhorses garners brief notoriety that leads to a full name such as “Metroliner.” But these full names, more often than not, are simply duplicated by analogy much in the same way the term “Watergate” has been overused. The identification of each fleet and the uniqueness / advancements that each have brought to the passenger rail industry since 1904 is the goal of this paper series. [1] With a pattern that was established with the first paper, this second paper “begins with the transition away from wooden cars when steel cars were necessary by design. These early cars helped to define the EMU and MU benchmark and how they differ from other rail rolling stock of the early 1900s such as elevated / subway cars, interurbans, and locomotive-drawn coach cars. Regulation was part of the progress, but ever-increasingly heavy passenger and mail / cargo loads, tunnel designs and general progress of design evolution helped to define this classification of rolling stock that eventually has folded into the United States-defined FRA (Federal Railroad Administration) Tier 1 passenger fleet.” [1] This paper picks up where the first paper left off by completing a review of the rail services that radiate from New York City; first to Southern Connecticut, and then into Northern New Jersey. A future, third paper will feature equipment serving Philadelphia along with the broader Northeast Corridor (NEC), and Chicago. And the fourth and final paper will feature a variety of other regions of the United States and Canada.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".