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Record W2152510918 · doi:10.5006/c2003-03563

MIC of Stainless Steel Pipes in Sewage Treatment Plants

2003· article· en· W2152510918 on OpenAlexaff
Troels Mathiesen, E. Rislund, Torben S. Nielsen, Jan Elkjaer Frantsen, Ulrich Tornaes, Michael Berggreen Petersen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsKruger (Canada)
Fundersnot available
KeywordsSewageCorrosionSewage treatmentMetallurgyMaterials scienceWaste managementEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Corrosion of stainless steel pipes in sewage treatment systems has been studied as part of a Danish research programme. The pipes were attacked by localized corrosion in locations near the final stage of the treatment process, where the water is practically free of organic substances. Since the temperature and chloride content is quite low, it has been difficult to explain the reason for corrosion. An extensive series of field-tests and inspections showed that the failures in most cases could be explained by bacterial deposition of manganese dioxide, which results in potential ennoblement due to its effective cathode properties. Preventive measures for this form of corrosion were evaluated with special focus on cathodic protection. Finally, revised design diagrams were established to support the selection of resistant stainless steel grades for low chloride environments affected by highly oxidizing conditions caused by bacteria.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.264
Teacher spread0.240 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations9
Published2003
Admission routes1
Has abstractyes

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