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Record W591502936

Burkhardt's Bangor Baby

2007· article· en· W591502936 on OpenAlexaboutno aff
K Kube

Bibliographic record

VenueTrains · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsBankruptcyRevenueSalaryMillFinanceLivelihoodTrack (disk drive)Economic historyManagementBusinessPolitical scienceEconomicsHistoryEngineeringLawArchaeologyMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

This article, third in a series on Ed Burkhardt’s rail world, this article focuses on the Montreal, Maine & Atlantic (MM&A) railroad. Prior to Burkhardt’s involvement, the railroad was run as the Bangor & Aroostook, with its origins dating back to 1891. Financial struggles resulted in its bankruptcy in 2002, and in early 2003, the railroad was acquired by Burkhardt’s Rail World and renamed the MMA. The article describes the struggles that the MM&A has faced in the four years since Burkhardt’s purchase. Key to MM&A’s livelihood was the Great Northern paper mill, which provided 25 percent of the railroad’s revenue. However, financial problems forced the mill’s shutdown, which then threw MM&A into salary reductions and layoffs. While new ownership has helped turn the tide for the mill, it is still not smooth sailing for the MM&A. The article describes how the railroad has purchased new maintenance of way equipment, upgraded track, and is prepared for adversity if necessary.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0800.024

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 head0.022
GPT teacher head0.218
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2007
Admission routes1
Has abstractyes

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