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Record W7005464138

Rail ports: The collaboration of railroad and coastal steamboat companies in New England in the 1840s

2016· article· en· W7005464138 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Media Literacy Education · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsTrainNew englandQuarter (Canadian coin)Rail networkRevenueSound (geography)
DOInot available

Abstract

fetched live from OpenAlex

Prior to the development of railroad travel in New England, the primary mode of transportation was the stagecoach, a slow and uncomfortable vehicle. Steamboats traveled between coastal cities, but were more limited in their routes than stagecoaches and averaged the same amount of travel time. Desire for faster travel times, specifically between New York City and Boston, accelerated technological advancements that led to the development of a fledgling railroad network in New England by the second quarter of the nineteenth century. It also led to increased collaboration between railroad and steamboat companies, each offering combination routes to save time. The Long Island Railroad Company (LIRRC) was one of many railroad companies that took part in this transportation revolution. It was also one of many to experience financial hardship and one of the few to escape complete failure following the economic Panic of 1837. LIRRC trains departed Brooklyn and arrived in Greenport, at the northeastern end of Long Island, three hours later. Passengers then took a ferry across Long Island Sound to either Stonington or Norwich, Connecticut and then continued to Boston by railroad. This route was poised to cut travel time between New York and Boston nearly in half, but lasted only two and a half years. This study will examine the impact of financial hardship on the LIRRC’s quest to offer the fastest route between New York and Boston.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.124

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.279
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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