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Record W4288988851 · doi:10.33423/jabe.v24i3.5289

Cross-Border Cargo Traffic Through a Rural Texas Port of Entry

2022· article· en· W4288988851 on OpenAlexvenueno aff
Thomas M. Fullerton, Steven L. Fullerton

Bibliographic record

VenueJournal of Applied Business and Economics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersUniversity of Texas at El PasoU.S. Department of TransportationNational Science Foundation
KeywordsMetropolitan areaPort (circuit theory)ExpeditingBusinessTruckCurrencyTraffic volumeInternational tradeEconomicsTransport engineeringGeographyEngineeringMonetary economics

Abstract

fetched live from OpenAlex

Most econometric analyses of cross-border traffic flows from Mexico have been conducted for the larger metropolitan economies along the international boundary. With the advent of the USMCA trade agreement, plus physical infrastructure bottlenecks at many ports of entry, ports of entry in smaller cities and towns are likely to play more important roles in expediting cross-border merchandise trade. To date, however, there has been very little formal analyses of the trade flows through many of these other, potentially key ports. This study attempts to partially fill part of that gap in the border economics literature by analyzing northbound cargo vehicle flows from Mexico to the United States through Ojinaga, Chihuahua and Presidio, Texas. Results indicate that the price of diesel fuel, United States business cycles, export manufacturing employment in Chihuahua City, and the inflation adjusted bilateral currency value of the peso influence the monthly volume of cargo trucks that use this border crossing facility.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.240
Teacher spread0.217 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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