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Record W1540221652

The Motorization of North America: causes, consequences, and speculations on possible futures

2001· preprint· en· W1540221652 on OpenAlexaboutno aff
Martín Wachs

Bibliographic record

VenueeScholarship (California Digital Library) · 2001
Typepreprint
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsTruckPopulationFutures contractGeographyCommercial vehicleBusinessEngineeringDemographyFinanceAutomotive engineeringSociology
DOInot available

Abstract

fetched live from OpenAlex

North America is the most motorized or automobile-oriented part of the world. This is shown in Table 1 using data for vehicle registrations in several parts of the world. While Africa has a population of 46 people per registered vehicle (including trucks and buses) or 70 persons per passenger automobile, and Asia has 26 people per vehicle or 41 per passenger car, North America has reached the point of having only 1.92 people per vehicle and 2.8 people per passenger car. These figures for North America include Mexico, a country that can still be said to be in the process of developing rapidly, and which has relatively low numbers of vehicles in relation to its population. In the United States there is one vehicle per 1.32 people, including commercial vehicles or one passenger car per 1.96 people, and Canada is similar with 2.18 persons per vehicle of every type of 1.92 people per passenger car (American Automobile manufacturers Association, 1997).

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.013
GPT teacher head0.210
Teacher spread0.197 · 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
Published2001
Admission routes1
Has abstractyes

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