{"id":"W4396954215","doi":"10.1080/13658816.2024.2351546","title":"Matching the building footprints of different vector spatial datasets at a similar scale based on one-class support vector machines","year":2024,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; State Key Laboratory of Geo-Information Engineering; National Natural Science Foundation of China","keywords":"Support vector machine; Scale (ratio); Matching (statistics); Class (philosophy); Vector (molecular biology); Data mining; Computer science; Geography; Cartography; Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008096641,0.001208842,0.001224597,0.002636785,0.0003679966,0.001079755,0.001539469,0.0007293897,0.0008339467],"category_scores_gemma":[0.002612059,0.0003441953,0.001216429,0.002304067,0.0003747086,0.001952256,0.001159103,0.001015428,0.0006292633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004875817,"about_ca_system_score_gemma":0.0005855784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00427716,"about_ca_topic_score_gemma":0.003951218,"domain_scores_codex":[0.9984687,0.0001400825,0.0001235179,0.0006048938,0.0005013462,0.000161434],"domain_scores_gemma":[0.9991473,0.000125016,0.0001434129,0.0001955198,0.0003363536,0.00005234874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002522742,0.0002616954,0.01475815,0.0001742114,0.0001646531,0.0001770411,0.0001553986,0.07443611,0.01840981,0.001843836,0.003863671,0.8855033],"study_design_scores_gemma":[0.00001098541,0.00007867019,0.007619671,0.00001565873,0.00002910958,0.0001133818,0.0001348861,0.9800409,0.008783994,0.001856446,0.001291002,0.00002538446],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1727558,0.0004603612,0.8201801,0.0001403452,0.0001246721,0.0001297649,0.0006412232,0.004065942,0.001501845],"genre_scores_gemma":[0.7935244,0.0002798997,0.2016211,0.0001039761,0.0000614637,0.0001678824,0.002744603,0.0000963077,0.001400346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00427716,"threshold_uncertainty_score":0.00850457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01478656171867773,"score_gpt":0.3017576699592746,"score_spread":0.2869711082405969,"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."}}