{"id":"W2025772950","doi":"10.1163/22134913-00002021","title":"Polygon-Based Drawing Accuracy Analysis and Positive/Negative Space","year":2014,"lang":"en","type":"article","venue":"Art & Perception","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Polygon (computer graphics); Landmark; Measure (data warehouse); Space (punctuation); Position (finance); Artificial intelligence; Orientation (vector space); Computer science; Mathematics; Dimension (graph theory); Computer vision; Pattern recognition (psychology); Geometry; Combinatorics; Data mining","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":[],"consensus_categories":[],"category_scores_codex":[0.0001014962,0.00009134228,0.000106872,0.0001563669,0.00008712967,0.00004355115,0.00002575501,0.00004821448,0.0002704861],"category_scores_gemma":[0.00005833148,0.0000964728,0.00005427438,0.000301801,0.00002449771,0.0001835128,0.00000607082,0.00007422958,0.0001764143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004027072,"about_ca_system_score_gemma":0.000003464161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007010825,"about_ca_topic_score_gemma":0.0001058084,"domain_scores_codex":[0.9995205,0.00004310884,0.0001029405,0.0001291227,0.00009867235,0.0001056875],"domain_scores_gemma":[0.9996857,0.0001093414,0.00002311874,0.00008438031,0.00004476657,0.00005270405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008704963,0.00008084135,0.03484872,0.00007316124,0.0004085966,0.000002617693,0.006803623,0.03265397,0.7231184,0.0003376679,0.001614066,0.1999713],"study_design_scores_gemma":[0.0003347472,0.00004226117,0.5709549,0.00002616242,0.0001675596,7.489995e-7,0.0001403693,0.4172933,0.0104115,0.00009760034,0.0003577005,0.0001731801],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8632638,0.000006727858,0.133626,0.0002090592,0.00005225021,0.00007841634,0.000006523559,0.0001609167,0.002596309],"genre_scores_gemma":[0.9982505,0.00001144217,0.001227206,0.0002353329,0.00007081968,0.000007594896,0.0001417061,0.00001212538,0.00004326238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7127069,"threshold_uncertainty_score":0.3934047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005940250374708617,"score_gpt":0.2278538510523191,"score_spread":0.2219136006776105,"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."}}