{"id":"W6996659948","doi":"","title":"Snapshot of Vancouver BC Open Data Catalogue v2","year":2011,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Global Urban Networks and Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Apartment; Downtown; Easement; Snapshot (computer storage); Real estate; Visitor pattern; Geographic information system; Cadastre; Documentation; Architecture","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":[0.001542093,0.001832869,0.001283477,0.01650691,0.003415805,0.008529942,0.00273699,0.0009987154,0.1900971],"category_scores_gemma":[0.008535998,0.001249957,0.0005755303,0.04179395,0.0005666815,0.002642613,0.003139678,0.001832886,0.186068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01196183,"about_ca_system_score_gemma":0.02665096,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.808521,"about_ca_topic_score_gemma":0.8090168,"domain_scores_codex":[0.9970466,0.0001714676,0.0002308156,0.0003939416,0.001757919,0.0003992947],"domain_scores_gemma":[0.9869332,0.0005431212,0.000407892,0.001477684,0.009328985,0.00130919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003227076,0.000006879201,0.0003721898,0.000149071,0.000005825982,0.00002243555,0.00006853457,0.00008847077,0.00009912878,0.0008117493,0.9843488,0.01399466],"study_design_scores_gemma":[0.00000783245,0.000002310865,0.002071866,0.00009440025,0.000003556785,0.0000181242,0.0001069104,0.00006626866,0.0001254588,0.0002805923,0.9972044,0.0000183159],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007371788,0.0004793128,0.0006492352,0.0003319879,0.0001798792,0.0001017798,0.9362679,0.003627347,0.05762537],"genre_scores_gemma":[0.001865446,0.0006442654,0.001710044,0.0001141631,0.00003802705,0.0001741224,0.9496023,0.001981018,0.04387061],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.191479,"threshold_uncertainty_score":0.635938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02117347824882579,"score_gpt":0.2345605509059771,"score_spread":0.2133870726571513,"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."}}