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Record W4401632442 · doi:10.22215/etd/2024-15986

(Re)Integrating Rammed Earth Construction Regulatory and Material Challenges and Opportunities of Rammed Earth Construction in Ontario

2024· dissertation· en· W4401632442 on OpenAlexaboutno aff
Nadia Kriplani

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsRammed earthMainstreamingEngineeringArchitectural engineeringCivil engineeringConstruction engineeringGeotechnical engineeringSociologyPedagogySpecial education

Abstract

fetched live from OpenAlex

Rammed earth construction is a promising, bio-based alternative to conventional carbon-intensive building materials. Despite this, it faces many obstacles to mainstreaming, such as a lack of integration into the regulatory framework in Ontario, concerns with growing embodied carbon in modern rammed earth, and an overall lack of awareness, acceptance, and education. Based on a literature review of international codes and standards, material explorations, and, guided by interviews with industry leaders (builder, engineer, consultant, and policy analyst), this thesis attempts to contribute to the mainstreaming of rammed earth construction in Ontario through a series of ‘micro-interventions’ addressing gaps in the field identified in the interview process. They include building digital models, submitting a Code Change Request for the NBCC, proposing a Rammed Earth Standard for Ontario, and creating a Rammed Earth Student Guidebook.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.772

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.007
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.021
GPT teacher head0.202
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2024
Admission routes1
Has abstractyes

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