{"id":"W2922006169","doi":"10.1139/er-2018-0105","title":"Mapping sources of atmospheric pollution: integrating spatial and cluster bibliometrics","year":2019,"lang":"en","type":"article","venue":"Environmental Reviews","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scopus; Bibliometrics; Air quality index; Air pollution; Apportionment; Cluster (spacecraft); Environmental science; Geography; Environmental resource management; Regional science; Data science; Meteorology; Political science; Computer science; Library science; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000249876,0.0001183524,0.0002358291,0.00001240422,0.00005282905,0.0000200625,0.0001020391,0.00004402561,0.003024097],"category_scores_gemma":[0.00002832435,0.00008836233,0.00006123578,0.0006486538,0.00007799099,0.0001268038,0.00002486529,0.00008975508,0.00024346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000533024,"about_ca_system_score_gemma":0.000004633818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001379334,"about_ca_topic_score_gemma":0.00001014332,"domain_scores_codex":[0.999182,0.00004623265,0.0002853359,0.0001918016,0.0001518419,0.0001427503],"domain_scores_gemma":[0.9995742,0.00005563227,0.0001715384,0.0001327877,5.419079e-7,0.00006532464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005006166,0.00000915323,0.7489995,0.00004681629,0.000005444213,7.449582e-7,0.0001273449,0.00004020324,0.001619302,4.170734e-7,0.00008551515,0.2490605],"study_design_scores_gemma":[0.000404451,0.0001432851,0.8126144,0.000164984,0.00001545627,0.0000339511,0.000770259,0.004893515,0.001200171,0.00002880651,0.1794293,0.0003014762],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761868,0.01988448,0.0005909057,0.00003766715,0.00008665137,0.0002116003,0.000009229781,0.000006717368,0.00298593],"genre_scores_gemma":[0.9893578,0.005481061,0.004295749,0.0001503287,0.0000551962,9.134019e-7,0.00002187117,0.000002743772,0.0006343769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.248759,"threshold_uncertainty_score":0.9978873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263578309715106,"score_gpt":0.2043221661676017,"score_spread":0.1916863830704506,"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."}}