{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.005011518,0.001233505,0.00132291,0.1624335,0.001325975,0.005806822,0.0007981019,0.0008102032,0.01027845],"category_scores_gemma":[0.04889062,0.0003053562,0.002321487,0.1493069,0.0005685011,0.003833268,0.002471436,0.0006494639,0.001339386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002559045,"about_ca_system_score_gemma":0.003700728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01073708,"about_ca_topic_score_gemma":0.0085588,"domain_scores_codex":[0.9930569,0.001846539,0.001470035,0.0009372064,0.002289022,0.0004002488],"domain_scores_gemma":[0.9536912,0.02780738,0.008494099,0.001682988,0.007293142,0.001031137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000754151,0.0002864343,0.3289894,0.02531231,0.003335248,0.001318552,0.008808036,0.009387246,0.003440257,0.03077197,0.1524067,0.4351897],"study_design_scores_gemma":[0.0002459852,0.0003176925,0.5972923,0.004353156,0.00209389,0.00139893,0.01229596,0.03138382,0.002241173,0.03373494,0.3143177,0.0003244414],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3849188,0.03290221,0.016933,0.009245986,0.0008791198,0.0018614,0.4760709,0.008769274,0.06841935],"genre_scores_gemma":[0.781149,0.01456653,0.06283086,0.0003001563,0.0006641598,0.002491214,0.1321058,0.0005825436,0.005309905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8375665,"threshold_uncertainty_score":0.03438491,"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."}}