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Record W7046446012

DESA1002 'Nine Quarter City' - <Eliza Morton>

2008· other· en· W7046446012 on OpenAlexaboutno aff

Bibliographic record

VenueThe Sydney eScholarship Repository (The University of Sydney) · 2008
Typeother
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)WeavingArchitectureScope (computer science)Process (computing)Focus (optics)RoofArchitectural design
DOInot available

Abstract

fetched live from OpenAlex

This semester has been a great experience. It provided a practical continuum from last semester’s task, with new challenges and obstacles to overcome and a focus on the development of architectural skills and understanding. The focus of the semester’s tasks, Isfahan in Iran, introduced me to a new world and culture. The challenges presented through designing a building in a country, about which I knew little, were numerous. To overcome so many unexpected challenges, I had to develop and hone my rudimentary skills. From the outset, I was enthralled by the city of Isfahan. Its fascinating culture, so different from that of Sydney, and the historic architecture laced throughout the city, provided great scope in the design process. I instantly wished to design a cultural building. The rug warehouse was a choice developed through discussion with the group. This gave me the opportunity to learn about an historically important activity and established a framework for the way in which the warehouse would function. Throughout the semester, I encountered problems, such as the selection of materials appropriate for the building and site, Isfahan’s climate and ensuring an efficient use of the space. My building reflects the process of rug weaving and the structure echoing the rug weaving process. The roof curves like a rolled up rug, almost as if a magic carpet. I progressively overcame these issues to develop an efficient design. I believe that the preliminary presentation, presenting to a different tutor, was highly beneficial as I had to stand back and evaluate my design in order to present it to someone who had not been involved in its development. It was also helpful to receive the insight and feedback of a person unfamiliar with the design as I entered the last phase of the project.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2008
Admission routes1
Has abstractyes

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