{"id":"W6950768390","doi":"10.5683/sp2/xxtau9","title":"Supplemental data for \"Air quality management for coastal urban centres using stochastic and machine learning techniques\"","year":2018,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Artificial neural network; Photogrammetry; Software; Air quality index; Air pollution; Drone","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001270508,0.002294064,0.001151243,0.001899283,0.0006418264,0.001299851,0.003683792,0.002146857,0.05706147],"category_scores_gemma":[0.003673945,0.0005798021,0.001390339,0.002507272,0.0004057362,0.001035501,0.001460357,0.001496727,0.04622496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001656374,"about_ca_system_score_gemma":0.00163715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04897808,"about_ca_topic_score_gemma":0.1469347,"domain_scores_codex":[0.9992657,0.0001058727,0.00007776041,0.0001819571,0.0002450204,0.0001236696],"domain_scores_gemma":[0.9982336,0.0003832825,0.000171398,0.0004245991,0.0006045562,0.0001826821],"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":[0.0000705621,0.00008997264,0.002389491,0.0004160834,0.00004782268,0.00004393447,0.00001900916,0.001907286,0.0001493246,0.0002567544,0.9901429,0.004466836],"study_design_scores_gemma":[0.0005514458,0.00009313167,0.01889142,0.0003659721,0.00007156094,0.0001590675,0.0001692389,0.01107774,0.001631318,0.002239471,0.9646639,0.00008568799],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006109116,0.00005259987,0.0002371704,0.0001125343,0.00008561112,0.00003413872,0.9971581,0.0008298538,0.0008791205],"genre_scores_gemma":[0.001663511,0.00002679313,0.0007266522,0.00003966162,0.00001164356,0.00006841584,0.9966816,0.00005614766,0.0007255041],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05706147,"threshold_uncertainty_score":0.1908896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.075831506273951,"score_gpt":0.3649104337872945,"score_spread":0.2890789275133435,"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."}}