MétaCan
Menu
Back to cohort
Record W788393592

Complex Cover-Up: Glass Flake Technology Is Central to the Paint Systems that Are Being Used on the Forth Rail Bridge in Scotland

2007· article· en· W788393592 on OpenAlexaboutno aff
Dave Bottomley

Bibliographic record

VenueBridge design & engineering · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsFirthBridge (graph theory)FlakePaintingCorrosionCathodic protectionEngineeringForensic engineeringQuarter (Canadian coin)Civil engineeringMetallurgyArchaeologyGeologyMaterials scienceHistoryVisual artsComposite materialAnodeOceanographyArt
DOInot available

Abstract

fetched live from OpenAlex

The author discusses Scotland's Forth Rail Bridge, a 117-year-old steel structure spanning the Firth of Forth. Due to its location, it is exposed to coastal conditions, including sea mists, high winds, and moderate to high salinity. To protect the bridge from corrosion, paint needs to be applied to 230,000 square meters of steel. During the bridge's first century, British Rail maintained its own painting staff, working primarily through brush application and hand tools. Bridge painting systems began to change with British Railways' privatization. Issues surrounding existing paint removal, including automated techniques, are discussed. The author discusses painting challenges faced by Railtrack, formed after privatization, and charged with protecting the bridge from corrosion for a quarter century. An insert discusses corrosion-proof glass use in glass flake coatings.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.046
GPT teacher head0.206
Teacher spread0.160 · 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
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueBridge design & engineeringSame topicTransport and Economic PoliciesFrench-language works237,207