{"id":"W2180305847","doi":"10.1260/1478-0771.13.2.217","title":"Harnessing Design Space: A Similarity-Based Exploration Method for Generative Design","year":2015,"lang":"en","type":"article","venue":"International Journal of Architectural Computing","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Networks of Centres of Excellence of Canada; Mitacs","keywords":"Cluster analysis; Computer science; Generative Design; Similarity (geometry); Data mining; Visualization; Parametric statistics; Parametric design; Space (punctuation); Machine learning; Artificial intelligence; Engineering; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00483995,0.001381806,0.001073604,0.00394581,0.001150895,0.003328277,0.002047298,0.001613515,0.007955994],"category_scores_gemma":[0.01346693,0.0009216106,0.002598707,0.002972946,0.003217631,0.004278757,0.004184409,0.001930843,0.001265274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060277,"about_ca_system_score_gemma":0.00159616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00121595,"about_ca_topic_score_gemma":0.002393264,"domain_scores_codex":[0.9959259,0.002263727,0.0002171946,0.000521936,0.0009431367,0.0001281859],"domain_scores_gemma":[0.9927296,0.005094096,0.0002907625,0.001226634,0.0004849649,0.0001739303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000258034,0.0002535773,0.002336245,0.00105234,0.0001886381,0.0003883825,0.01007306,0.06192772,0.01128525,0.4796294,0.005589213,0.4270182],"study_design_scores_gemma":[0.0001416421,0.0002169627,0.0006084553,0.000327088,0.00008689125,0.0005862028,0.001300477,0.4821656,0.005407453,0.4688643,0.040194,0.0001009831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002440138,0.00008755085,0.99533,0.0001119662,0.000009712901,0.00009945592,0.00005089055,0.000310387,0.00155984],"genre_scores_gemma":[0.03809224,0.00007960387,0.960207,0.00003932047,0.000009446457,0.000371896,0.0001179031,0.0001298006,0.0009527159],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007955994,"threshold_uncertainty_score":0.02661544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1626037918793429,"score_gpt":0.4022945369658353,"score_spread":0.2396907450864924,"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."}}