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Record W2948566981 · doi:10.22215/etd/2019-13425

Culinary Topology: Rethinking the Parking Lot Threshold at the Montreal Hospital

2019· dissertation· en· W2948566981 on OpenAlexaboutno aff
Oliver Y. Tang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsParking lotSpace (punctuation)Process (computing)Gateway (web page)Architectural engineeringEngineeringBusinessComputer scienceAdvertisingCivil engineeringWorld Wide Web

Abstract

fetched live from OpenAlex

The parking lot is the arrival area for many people entering the hospital. This space has an opportunity to become something more than a featureless utilitarian space for cars, it could offer an integrated experience of 'threshold'. This thesis uses culinary theory as an architectural tool to redefine the parking lot as 'threshold'. Cuisine will offer a metaphorical design language that will act as interlocutor in the design process of rethinking the arrival space between car and hospital.Cooking is an appropriate guide for this process - it is an act which transforms raw materials through acts of combinations, manipulations and deformations toward a delightful end. Three cooking elements: blanching, sauce and soufflé are used to transform the physical elements of the underground entry threshold at the hospital with an aim to make the experience more appropriate as a gateway between sickness and health.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.902
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.012
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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
Published2019
Admission routes1
Has abstractyes

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