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Lessons Learned from the Treat Island Marine Exposure Site

2016· article· en· W2521944636 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKey engineering materials · 2016
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsCorrosionAir entrainmentEttringiteChlorideEnvironmental scienceReinforcementCementMaterials scienceGeotechnical engineeringComposite materialGeologyMetallurgyPortland cement

Abstract

fetched live from OpenAlex

The marine exposure site on Treat Island near Eastport, Maine, was built more than 75 years ago and during this period a wide range of concrete types have been placed on the site. Treat Island represents a very severe exposure condition with the highest tides in the world, salinity typical of the Atlantic Ocean and approximately 100 freeze-thaw cycles per annum. The various research programs that have used this facility have investigated the effects of numerous parameters including fibre-reinforcement, polymer-impregnation, supplementary cementing materials, sulfur concrete, high-alumina cement, ettringite-based rapid-set binders, w/cm and strength, ultra-high-performance concrete, corrosion-resistant reinforcement, impact of load and cracking, “mechanical air-entrainment”, and use of corrosion-inhibiting admixtures. Performance has been evaluated in a number of ways including visual assessment, pulse velocity, dynamic modulus, chloride profiling, and electro-chemical corrosion monitoring. The paper presents an overview of “lessons learned” with detailed information on factors affecting the rate of chloride ingress.

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.

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 score0.147
Threshold uncertainty score0.998

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)0.0030.000

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 head0.020
GPT teacher head0.210
Teacher spread0.190 · 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