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Record W3174217912 · doi:10.32866/001c.24958

The Impact of COVID-19 on the Movement of Trucks Between Canada and the US: Evidence from the Ambassador Bridge

2021· article· en· W3174217912 on OpenAlexaffabout
Hanna Maoh, William Anderson

Bibliographic record

VenueFindings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTruckCoronavirus disease 2019 (COVID-19)Bridge (graph theory)Pandemic2019-20 coronavirus outbreakGeographyTraffic volumeSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Transport engineeringEngineeringMedicineVirologyOutbreak

Abstract

fetched live from OpenAlex

The ongoing COVID-19 pandemic has closed land borders for non-essential travel between Canada and the US since the middle of March 2020. This paper explores if the pandemic has had a significant impact on the patterns of truck movements between the two countries. The volume of trucks that crossed the Ambassador Bridge (busiest land border in Canada) between August 1, 2019 and February 28, 2021 is analyzed. We found that truck volume dropped only during the first two months of the pandemic. Truck traffic increased significantly in May to reach higher than normal levels and then normalized after November 2020.

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)0.004
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 score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.332
Teacher spread0.281 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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