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Record W6995502694

Daren Pelipel

2020· article· en· W6995502694 on OpenAlexaboutno aff

Bibliographic record

VenueSOURCE Sheridan's Institutional Repository (Sheridan College) · 2020
Typearticle
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsBlock (permutation group theory)MasonryOffset (computer science)Work (physics)Style (visual arts)
DOInot available

Abstract

fetched live from OpenAlex

It takes a strong mind and body to successfully complete the work of a firefighter’s; much like how it takes a strong non-combustible material such as raw concrete and masonry block to prevent fires from spreading. This redesign of Brampton Station 204 embodies strength through these materials, which can commonly be found in Brutalist architecture, a style which can be perceived as bold. While Brutalist styles aren’t known for being the most pleasing aesthetic, Station 204 finds a way to offset its critiques with bright and colourful accents in its interior and with a deep-grey, textured exterior.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.195
Threshold uncertainty score0.652

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1950.102

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 head0.014
GPT teacher head0.196
Teacher spread0.182 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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