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Record W1982640211 · doi:10.1068/b31175

Landscape Grammar 1: Spatial Grammar Theory and Landscape Planning

2005· article· en· W1982640211 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

VenueEnvironment and Planning B Planning and Design · 2005
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGrammarGenerative grammarComputer scienceEmergent grammarEcotopeLinguisticsGeneralized phrase structure grammarVocabularyLandscape designAffix grammarHead-driven phrase structure grammarRelational grammarNatural language processingArtificial intelligenceLandscape ecologyEngineering

Abstract

fetched live from OpenAlex

This paper presents the concept of a spatial landscape grammar. The concept formally draws parallels between the structures of linguistics and the character of real-world landscapes. Landscape grammar can be used to define a landscape's character by using a vocabulary of landscape object types and spatial syntax rules, and these can be used to generate landscape scenes rendered in two or three dimensions through the use of a generative and interpretive production system and modern computing technology. The spatial counterparts of the linguistic concepts of vocabulary and grammar rules are formalized and the basis of the landscape production system is presented. The paper concludes with a short discussion of actual landscape scene generation as a prelude to a companion paper that describes a full implementation of the grammar and interpreter for a residential neighbourhood in Bermuda.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.229
Teacher spread0.211 · 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