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Record W2920042566 · doi:10.2749/vancouver.2017.2083

The New Champlain Bridge: Technical Challenges in Design of the Approach Viaducts

2017· article· en· W2920042566 on OpenAlexaff
Zachary McGain, Damien Goulmot, Sevak Demirdjian, M. Tremblay

Bibliographic record

VenueReport · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsBridge (graph theory)ScheduleComputer scienceConstruction engineeringQuality (philosophy)Civil engineeringTransport engineeringArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

The New Champlain Bridge is an iconic structure, being delivered on an extremely fast tracked schedule. Add to that numerous limitations on construction activities arising from the sensitive river environment, difficult climatic conditions, and concerns over existing regional infrastructure. Finally, throw in strict visual quality requirements to achieve a pre-published architectural concept. This paper discusses how the above conditions guided decision making and design concepts for the approach structures and specific design challenges encountered while meeting the expectations of the constructor, concessionaire and the Authority.

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)0.003
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: Other · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.062
GPT teacher head0.264
Teacher spread0.202 · 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
GenreOther

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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