{"id":"W2023635708","doi":"10.1145/1183471.1183499","title":"Object localization based on directional information case of 2D vector data","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rectangle; Object (grammar); Raster graphics; Object-based spatial database; Raster data; Computer science; Computation; Spatial analysis; Algorithm; Mathematics; Theoretical computer science; Artificial intelligence; Spatial database; Geometry","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":[0.001120766,0.0003949896,0.0008332919,0.001603052,0.0005179262,0.001876016,0.001487754,0.0014361,0.002968722],"category_scores_gemma":[0.007656017,0.0004475084,0.001110591,0.002744936,0.001283509,0.003819799,0.001564147,0.0009113495,0.000566543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001178066,"about_ca_system_score_gemma":0.000650019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01317777,"about_ca_topic_score_gemma":0.006113442,"domain_scores_codex":[0.9987766,0.000228726,0.00007384009,0.0003011006,0.000445054,0.0001746265],"domain_scores_gemma":[0.9980798,0.0009597412,0.0002009561,0.0004314659,0.0002449928,0.00008311176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003482242,0.00006568058,0.007563024,0.0003621463,0.00007274721,0.00206013,0.0008996314,0.5241915,0.007728296,0.3379597,0.00436297,0.114386],"study_design_scores_gemma":[0.000011618,0.00002691603,0.00104901,0.00001459995,0.00001098157,0.0005105034,0.00007922735,0.9506975,0.001212254,0.0447761,0.00158913,0.00002214395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05182939,0.0004379001,0.9410674,0.0005038657,0.00004080446,0.00005352557,0.0006777914,0.0003319557,0.005057367],"genre_scores_gemma":[0.7114738,0.0007611275,0.2825124,0.0001806596,0.00009637628,0.0002008896,0.001012155,0.00008708733,0.003675568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01317777,"threshold_uncertainty_score":0.02620208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01638206633427629,"score_gpt":0.2392124419422057,"score_spread":0.2228303756079295,"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."}}