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Record W1950755087 · doi:10.1520/stp10881s

Field Exposure Results on Trends in Atmospheric Corrosion and Pollution

2002· book-chapter· en· W1950755087 on OpenAlexaboutno aff
Johan Tidblad, V. Kučera, Alexander Mikhailov, Jan F. Henriksen, Kateřina Kreislová, Tom Yates, Brett Singer

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceAtmospheric pollutionField (mathematics)PollutionCorrosionMetallurgyMaterials scienceMathematics

Abstract

fetched live from OpenAlex

The International Co-operative Programme on Effects on Materials including Historic and Cultural Monuments (ICP Materials) is an extensive field exposure programme within the United Nations Economic Commission for Europe (UN ECE). In its network of test sites, which presently includes 30 test sites in 14 European countries and in Israel, the United States and Canada, several one-year exposures of unalloyed carbon steel, zinc, copper, cast bronze, limestone and steel panel with alkyd paint have been performed during the period 1987–97. The present work summarizes and analyses the one-year exposures for trend effects in Europe and, in particular, quantifies the part of the trend attributable to changes in sulfur dioxide concentration. SO2 is the largest single contributing factor to the decreasing corrosion trends. The decreasing H+ in precipitation is also a contributing factor, its effect is, however, much smaller than that of dry deposition. In addition to the ICP Materials results, long term trend examples of zinc and carbon steel corrosion and SO2 concentration for the period 1946–1997 are shown for Stockholm, Moscow, Prague and Kopisty.

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)0.000
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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

Citations8
Published2002
Admission routes1
Has abstractyes

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