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Record W2469810256

Amec Foster Wheeler

2016· article· en· W2469810256 on OpenAlexvenueaboutno aff
Frank Babic, Buddy Ledger, Alfredo Rodrigues

Bibliographic record

VenueCanadian acoustics · 2016
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsNoise (video)Environmental noiseAcousticsNoise controlSound (geography)EngineeringFossil fuelCivil engineeringBusinessComputer scienceNoise reductionPhysicsWaste managementArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Amec Foster Wheeler is a global consulting engineering company that employs over 40,000 people in more than 55 countries.The Acoustic Centre of Expertise is a group within Amec Foster Wheeler that supports acoustics, noise and vibration engineering services.We are based out of the Mississauga, Ontario, Canada and support projects throughout North and South America.Our expertise includes environmental noise, transportation noise, building acoustics, vibration and monitoring in support of our oil and gas, clean energy, environmental and infrastructure and mining markets.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.446
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5540.296

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 head0.014
GPT teacher head0.159
Teacher spread0.144 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations2
Published2016
Admission routes2
Has abstractyes

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