{"id":"W4408128458","doi":"10.2196/preprints.56692","title":"Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-Term Care for Older People: Comprehensive Analysis Through Bibliometric, Google Trends, and Content Analysis (Preprint)","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Impact of AI and Big Data on Business and Society","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Enhanced Data Rates for GSM Evolution; The Internet; Term (time); Computer science; Content analysis; Data science; Internet privacy; World Wide Web; Artificial intelligence; Sociology; Social science","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":"design_other","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":["metaepi_narrow","bibliometrics","scholarly_communication"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.001416255,0.0005387672,0.002475442,0.03170439,0.00007330863,0.001072669,0.001010352,0.0004756877,0.0004674418],"category_scores_gemma":[0.0009035157,0.0003826209,0.001230156,0.06740285,0.0004447654,0.0006088808,0.002944047,0.000508567,0.00000341109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006924494,"about_ca_system_score_gemma":0.000125849,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00867514,"about_ca_topic_score_gemma":0.004319906,"domain_scores_codex":[0.9940345,0.0001818628,0.002505514,0.001626671,0.001245629,0.0004058068],"domain_scores_gemma":[0.9933684,0.002196023,0.001079471,0.001176035,0.002020221,0.0001597831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00021669,0.0003412659,0.3389741,0.001063962,0.004798993,0.00001006443,0.02666297,0.002369754,0.00006958628,0.002387367,0.000313944,0.6227913],"study_design_scores_gemma":[0.0001500355,0.0001697976,0.8911554,0.0003843314,0.00453141,0.000003115137,0.01960078,0.07547608,0.002801078,0.004942292,0.00008798396,0.0006976968],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6498939,0.004839831,0.3434082,0.0001877413,0.0002902884,0.0005258379,0.0007662677,0.0000216036,0.00006625311],"genre_scores_gemma":[0.9896008,0.003466734,0.006061788,0.00007830755,0.00003153108,0.00003255253,0.0004426387,0.00001900238,0.0002666792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6220936,"threshold_uncertainty_score":0.9999643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2619095506350165,"score_gpt":0.4214502357368264,"score_spread":0.1595406851018099,"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."}}