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The damage rating index (DRI): A practical guideline for autonomous operator training

2025· article· en· W4414760183 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRILEM Technical Letters · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWork (physics)Operator (biology)GuidelineIndex (typography)Rating system

Abstract

fetched live from OpenAlex

The damage rating index (DRI) is a microscopy tool that captures the extent of internal swelling reaction-induced deterioration (ISR). Although engineering practitioners more widely use mechanical tests, confirming the presence of ISR products through microscopy is required and standard practice. A more detailed evaluation can be achieved by combining mechanical and microscopy techniques, including the DRI, which has proven reliable in diagnosing the extent of ISR-induced deterioration. However, there is currently a lack of practical guidelines and standards in the literature explaining how to perform the DRI, raising concerns about the tool's use, particularly regarding operator variability and subjectivity. This work aims to create practical guidelines for conducting the DRI analysis methodology on concrete affected by alkali-silica reaction (ASR) originating from either reactive coarse or fine aggregates at various degrees of damage (i.e., 0.05%, 0.12%, 0.20%, and 0.30% expansion). Ranges of expected values were established to serve as autonomous training for new operators using the same reactive aggregates and mixtures.

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)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.097
GPT teacher head0.528
Teacher spread0.431 · 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