{"id":"W4403462052","doi":"10.2139/ssrn.4952048","title":"Human Intelligent-Things Interaction Application Using 6G and Deep Edge Learning","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Computer science; Artificial intelligence; Human interaction; Deep learning; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007490697,0.0001155974,0.0000968242,0.0005077118,0.000529536,0.0003807544,0.0001297337,0.00008572013,0.00002982126],"category_scores_gemma":[0.00003634498,0.0001119216,0.00004477801,0.0005141989,0.00005281211,0.001407451,0.00007218466,0.001918329,0.000079313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002634584,"about_ca_system_score_gemma":0.00006982771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002458799,"about_ca_topic_score_gemma":0.000288492,"domain_scores_codex":[0.9988419,0.000008012612,0.0002351412,0.0001814902,0.00009839774,0.0006350229],"domain_scores_gemma":[0.999675,0.00001537833,0.0001372242,0.00008012068,0.00008605326,0.000006184316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004018982,0.00002105429,0.003997179,0.00003532197,0.00008348635,0.000002290938,0.00006483276,0.00006532992,0.005926584,0.8469647,0.00005581065,0.1427794],"study_design_scores_gemma":[0.0003785251,0.00007633815,0.0006717923,0.0002164531,0.0002721163,0.0006247599,0.01013747,0.0916331,0.0003071727,0.7750868,0.120069,0.0005264067],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8304448,0.001873419,0.1613427,0.003060262,0.0003152718,0.0001337689,8.44832e-8,0.0003154874,0.002514231],"genre_scores_gemma":[0.9983439,0.0002225281,0.0000630939,0.0002945939,0.0007535537,0.000005517218,0.000009935926,0.00002619336,0.0002806395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1678991,"threshold_uncertainty_score":0.8334293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01490069515553842,"score_gpt":0.2740059181778287,"score_spread":0.2591052230222903,"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."}}