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Record W6982670618

Is RFID the Answer to Resurgent Border Traffic?

2012· article· en· W6982670618 on OpenAlexaboutno aff

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2012
Typearticle
Languageen
FieldMathematics
TopicCensus and Population Estimation
Canadian institutionsnot available
Fundersnot available
KeywordsTraffic volumeVolume (thermodynamics)Road trafficWork (physics)Travel time
DOInot available

Abstract

fetched live from OpenAlex

With respect to cross-border passenger travel at Blaine, Washington (the I-5 corridor), two things were evident in the aftermath of 9/11—the volume of travel dropped dramatically, and the at-booth inspection process became more time-consuming. The combined effect was that wait-times remained roughly comparable to what existed pre-9/11, despite traffic volumes that were 25 percent lower. The constant worry, though, was “How will we cope when traffic volumes climb?” For eight years regional stakeholders pursued initiatives intended to reduce wait-times, even as traffic volumes languished at an average volume of about 215,000 cars per month. The tail end of that eight-year period is seen in the left half of Figure 1, and the long-anticipated resurgence of traffic is evident in the right half, dating roughly from autumn 2009. In the first ten months of 2011 (highlighted in the figure), traffic reached an average level of 358,000 cars per month, a level not observed since 1997. The vast majority of those travelers were Canadian residents.

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)0.000
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 score0.793
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.067
GPT teacher head0.324
Teacher spread0.257 · 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
Published2012
Admission routes1
Has abstractyes

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