{"id":"W4280490323","doi":"10.5194/isprs-annals-v-4-2022-83-2022","title":"URBAN FUNCTIONAL ZONE IDENTIFICATION BY CONSIDERING THE HETEROGENEOUS DISTRIBUTION OF POINTS OF INTERESTS","year":2022,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Beijing University of Civil Engineering and Architecture; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Identification (biology); Cluster analysis; Pairwise comparison; Scale (ratio); Data mining; Point of interest; Artificial intelligence; Spatial analysis; Pattern recognition (psychology); Information retrieval; Geography; Cartography","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":[0.002774672,0.00006955367,0.0001441694,0.0001068412,0.001188547,0.00007816073,0.0002389801,0.00003045792,0.00003058449],"category_scores_gemma":[0.0005983485,0.00005087856,0.000114409,0.0007687915,0.001118487,0.0001549729,0.00008809346,0.00009529725,5.17575e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002556971,"about_ca_system_score_gemma":0.0001243971,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2754003,"about_ca_topic_score_gemma":0.02882543,"domain_scores_codex":[0.9981332,0.0003643796,0.0005382689,0.000108006,0.0007137498,0.0001423425],"domain_scores_gemma":[0.998534,0.0002294427,0.0006810955,0.0001778241,0.000340526,0.00003709993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005092035,0.00004979076,0.001259618,0.0000389678,0.00005154175,6.078361e-8,0.01068314,0.005426861,0.001902841,0.0001707224,0.001773157,0.9785924],"study_design_scores_gemma":[0.0002936286,0.00020838,0.007253587,0.00008274271,0.00007213832,0.0000066978,0.02956635,0.8726095,0.07195516,0.002263159,0.01545256,0.0002360894],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7345527,0.000102154,0.261369,0.003179472,0.0002642788,0.000245384,0.00009735578,0.00001241219,0.0001772231],"genre_scores_gemma":[0.9996911,0.00003046098,0.00002240523,0.0001796407,0.00001758583,4.734323e-7,0.00003056619,0.000001611103,0.00002615276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9783563,"threshold_uncertainty_score":0.988896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04563765799159491,"score_gpt":0.3065689675907988,"score_spread":0.2609313095992039,"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."}}