{"id":"W6927587193","doi":"10.3389/fneur.2023.1237839.s003","title":"Presentation_2_Smartphone use in Neurology: a bibliometric analysis and visualization of things to come.PPTX","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Relevance (law); Mobile phone; Bibliometrics; Scientific literature; Field (mathematics); Phone; Visualization; Citation; Focus (optics)","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":["insufficient_payload"],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"not_applicable","genre":"other","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":[0.003724059,0.0006487315,0.001087338,0.0338171,0.0006136824,0.004650355,0.0008833691,0.0006522121,0.1483667],"category_scores_gemma":[0.0232367,0.000284078,0.001985935,0.04927381,0.000337066,0.003958997,0.002911274,0.0009202303,0.01525138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00130227,"about_ca_system_score_gemma":0.002282633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00360556,"about_ca_topic_score_gemma":0.00549173,"domain_scores_codex":[0.9976707,0.0004465453,0.0002888766,0.0002249351,0.001167506,0.0002013481],"domain_scores_gemma":[0.98406,0.008801938,0.002291048,0.0004834538,0.003887089,0.0004764576],"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":[0.0002169705,0.00007234397,0.02242452,0.01534285,0.0006183716,0.0002516771,0.001730488,0.0006787722,0.0009537814,0.004920026,0.8105883,0.1422019],"study_design_scores_gemma":[0.0001379996,0.0002442185,0.1630144,0.00867126,0.0006406087,0.0006181993,0.004503746,0.002760467,0.001085743,0.007121881,0.8110873,0.0001142462],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.05394327,0.01406422,0.007534154,0.03361725,0.005212876,0.002903254,0.7827284,0.008688372,0.09130815],"genre_scores_gemma":[0.3502484,0.03061948,0.04673525,0.007902062,0.009025882,0.006879381,0.4545654,0.004323752,0.08970042],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9661829,"threshold_uncertainty_score":0.4963361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1337374311889245,"score_gpt":0.4813603405864543,"score_spread":0.3476229093975298,"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."}}