{"id":"W4285728602","doi":"10.3389/fpsyt.2022.884600","title":"A bibliometrics analysis and visualization of autism spectrum disorder","year":2022,"lang":"en","type":"article","venue":"Frontiers in Psychiatry","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"State Administration of Traditional Chinese Medicine of the People's Republic of China; National Natural Science Foundation of China","keywords":"Autism spectrum disorder; Visualization; Bibliometrics; Autism; Psychology; Medicine; Clinical psychology; Psychiatry; Computer science; Artificial intelligence; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0003766709,0.0001269429,0.0002910238,0.01585606,0.0002108091,0.0000348518,0.0003781715,0.00004453355,0.0002931549],"category_scores_gemma":[0.00008860534,0.0001437833,0.00009181432,0.05312324,0.0001330703,0.0001456021,0.0002906071,0.0002563728,0.000001712353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007386006,"about_ca_system_score_gemma":0.00009211726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001656781,"about_ca_topic_score_gemma":0.00007967961,"domain_scores_codex":[0.9978655,0.0002813357,0.0003342048,0.0004882108,0.000682764,0.0003479726],"domain_scores_gemma":[0.9994007,0.00005339629,0.000150122,0.0003266696,0.00000270821,0.00006644097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004676708,0.000316531,0.9313805,0.00002706331,0.00003802214,0.00000360267,0.0003125151,0.0005270852,0.0001041766,0.06399276,0.002377261,0.0008737785],"study_design_scores_gemma":[0.001503618,0.0003449835,0.7727851,0.000007135605,0.00009933567,0.000009871778,0.0008786509,0.03428481,0.0004729991,0.1869025,0.00232709,0.0003839892],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8802556,0.003658793,0.1023113,0.007524713,0.00362214,0.0005012114,0.00007944958,0.0000795306,0.001967317],"genre_scores_gemma":[0.9972776,0.0005194277,0.001785413,0.0001526991,0.00001100489,0.00003794422,0.000008606624,0.00002344449,0.0001838167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1585954,"threshold_uncertainty_score":0.9952984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0129857973162451,"score_gpt":0.2942900300952375,"score_spread":0.2813042327789924,"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."}}