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Record W2935983339 · doi:10.11575/prism/32267

On-It Summer 2017 Calgary to Banff Transit Pilot … A Huge Success!

2017· article· en· W2935983339 on OpenAlexaboutno aff
Calgary Regional Partnership, Richard White

Bibliographic record

VenuePRISM (University of Calgary) · 2017
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsTransit (satellite)GeographyMeteorologyTransport engineeringEngineeringPublic transport

Abstract

fetched live from OpenAlex

According to Mayor Karen Sorensen of Banff Alberta, “For the first time in our history, visitors from Calgary had the opportunity to travel to Banff on convenient, inexpensive and comfortable mass transit. We want to ensure this is the way of the future – that more and more visitors will choose to leave their private vehicles at home and hop on a bus, or a train, to travel to and within the Banff National Park and the Town. We were proud to partner with Parks Canada on this transit initiative and collaborate with Calgary Regional Partnership and Town of Canmore to bring On-It to Banff.”

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.669
Threshold uncertainty score0.659

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1690.036

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 head0.211
Teacher spread0.198 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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