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Record W2093677625 · doi:10.3141/2133-11

Calibrating a Synthetic Built Form Generator

2009· article· en· W2093677625 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

VenueTransportation Research Record Journal of the Transportation Research Board · 2009
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsTrusted Positioning (Canada)University of Calgary
Fundersnot available
KeywordsMetropolitan areaComputer scienceSortingGenerator (circuit theory)Process (computing)Land useTransport engineeringSpace (punctuation)Operations researchData miningCivil engineeringGeographyEngineeringAlgorithm

Abstract

fetched live from OpenAlex

A system for assigning space (buildings) to parcels to establish a base-year parcel-level description of built form is described. The system was applied repeatedly to Autauga County, Alabama, where a land use–transport interaction model is being developed. The system sorts parcels according to suitability for different space types, with the details of the sorting process controlled by user parameters. Parameters were adjusted to achieve appropriate assignment in one county for which target data were available to compare the assignment with observed data. Three map comparison techniques were applied. The resulting parameters will be used in the other counties in the Montgomery, Alabama, Metropolitan Planning Organization and may be transferable to other areas in the United States. Major findings include the importance of an accurate zonal-level inventory, the usefulness of quantitative map comparisons, and the need for some information to identify vacant parcels.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.062
GPT teacher head0.327
Teacher spread0.265 · 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