{"id":"W1982640211","doi":"10.1068/b31175","title":"Landscape Grammar 1: Spatial Grammar Theory and Landscape Planning","year":2005,"lang":"en","type":"article","venue":"Environment and Planning B Planning and Design","topic":"Design Education and Practice","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Grammar; Generative grammar; Computer science; Emergent grammar; Ecotope; Linguistics; Generalized phrase structure grammar; Vocabulary; Landscape design; Affix grammar; Head-driven phrase structure grammar; Relational grammar; Natural language processing; Artificial intelligence; Landscape ecology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009574956,0.0003302936,0.0002723062,0.0001586001,0.0002995061,0.0001976597,0.00008599459,0.0001659338,0.0001337139],"category_scores_gemma":[0.00006190378,0.0003184227,0.00002595447,0.0000608203,0.00007507582,0.0002995234,0.00004001762,0.0003598584,0.00003015794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001641401,"about_ca_system_score_gemma":0.00001355271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006040635,"about_ca_topic_score_gemma":2.151254e-7,"domain_scores_codex":[0.9986038,0.0001844502,0.0002618809,0.000361906,0.0001860373,0.0004019171],"domain_scores_gemma":[0.9986008,0.0009141758,0.00007138392,0.0001650381,0.000005760675,0.0002428711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001560393,0.0001816628,0.3647841,0.0003458737,0.0005945625,0.0003337641,0.03962654,0.3451726,0.003775939,0.001628146,0.06433691,0.1776596],"study_design_scores_gemma":[0.005008331,0.0007838482,0.2355471,0.0007072359,0.0005505374,0.0008120704,0.009729053,0.2554184,0.001687495,0.002485605,0.4839493,0.003320996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5485477,0.05186586,0.3866562,0.0003648589,0.0005786594,0.0005497138,0.00002309493,0.0006506575,0.01076325],"genre_scores_gemma":[0.9913334,0.0005633887,0.006777979,0.0002106095,0.0004340096,0.00002373602,0.00004820519,0.00005074112,0.0005579842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4427857,"threshold_uncertainty_score":0.9999268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01851823600550002,"score_gpt":0.2291244590086759,"score_spread":0.2106062230031759,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}