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Record W3129089658 · doi:10.2749/vancouver.2017.2483

The 5% Solution

2017· article· en· W3129089658 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

VenueReport · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Sustainable Development
Canadian institutionsNordion (Canada)
Fundersnot available
KeywordsCarbon footprintTonneReduction (mathematics)Production (economics)FootprintMetreStructural integrityField (mathematics)Civil engineeringEnvironmental scienceEngineeringComputer scienceWaste managementGreenhouse gasStructural engineeringMathematicsGeology

Abstract

fetched live from OpenAlex

<p>Opportunities abound in the field of structural design to make a meaningful contribution towards the reduction of our carbon footprint. Typical construction materials—steel and concrete—are among the highest CO2-emitting materials during their production. Production of one tonne of steel emits 1.8 tonnes of CO2, and production of one cubic meter of concrete, on average, emits 250 kg of CO2. A modest reduction in the use of steel and concrete in structural designs will go a long way in reducing CO2 emissions. The analysis of structural designs by us and other authorities shows that a reduction of 5% of steel and 5% of concrete in a building can be achieved without impacting the structural integrity by just being a little more judicious while designing. Being environmentally mindful while designing structural elements is what we call the 5% Solution.</p>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.772

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 studies0.0010.000
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 head0.026
GPT teacher head0.277
Teacher spread0.251 · 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