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Record W3202090278 · doi:10.5006/3824

Corrosion Behavior of Unprotected Weldments in Marine Environments

2021· article· en· W3202090278 on OpenAlexaffabout
Nicholas A. Senior, L. D. Parkinson, Magdalene Matchim, Jennifer Collier, Hung‐Wei Liu

Bibliographic record

VenueCORROSION · 2021
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsConsumablesCorrosionWeldingMetallurgyCathodic protectionMaterials scienceHeat-affected zoneElectrochemistryElectrode

Abstract

fetched live from OpenAlex

In the absence of protective measures such as paint or cathodic protection systems, steel weldments, immersed in seawater, are expected to corrode freely. This is particularly true for the ice breakers serving the Canadian Coast Guard, where, in the course of operations, paint is scoured from the vessel hulls and cathodic protection systems were not installed. However, the weldments do not corrode uniformly. In some cases, the weld itself corrodes rapidly and requires regular replacement. At the other extreme, the heat-affected zones (HAZ) corrode instead—a potential safety and integrity concern. The morphology of ice breaker weldment corrosion has altered over the last few decades and this has been attributed to changes in welding consumables and processes. The current study is an investigation into the corrosion characteristics of weldments with a particular focus on the compositional differences between weld metal and hull plate steels. A method has been developed for numerically describing the corrosion of weldment regions (plate steel, HAZ, weld cap passes, and weld re-heated zones) arising from an accelerated corrosion test. This in turn enabled the development of an equation that predicts weldment corrosion performance based entirely on material composition. This permits selection of welding consumables that are anticipated to give good corrosion performance, avoiding the extremes of rapid weld metal corrosion and preferential HAZ attack.

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 categoriesnone
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 score0.017
Threshold uncertainty score0.388

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)0.0000.000

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2021
Admission routes2
Has abstractyes

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