{"id":"W4408279608","doi":"10.2139/ssrn.5173037","title":"Machine Learning-Optimized Wearable Antenna Enhancing Wbans Medical Applications","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Wearable computer; Computer science; Antenna (radio); Wearable technology; Embedded system; Human–computer interaction; Telecommunications","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002417399,0.000544936,0.0007388403,0.0003544474,0.0003546997,0.0001460676,0.001099115,0.0007902849,0.0002221825],"category_scores_gemma":[0.0001304091,0.0005634319,0.0003545888,0.0003592451,0.00006783019,0.000102226,0.0003831424,0.01868557,0.00005795987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002023832,"about_ca_system_score_gemma":0.003812803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001000002,"about_ca_topic_score_gemma":0.001247433,"domain_scores_codex":[0.9947799,0.00017319,0.0008299637,0.0004756058,0.0006674599,0.003073912],"domain_scores_gemma":[0.9986877,0.0001835317,0.0002164109,0.0005030614,0.0001374339,0.0002718294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007538524,0.0001234547,0.0003369735,0.0004144277,0.002042861,0.00003394093,0.000254805,0.9098123,0.0002934656,0.01899075,0.0004485802,0.06717308],"study_design_scores_gemma":[0.001291231,0.00006519834,0.00002047504,0.001138186,0.0002481586,0.0003835326,0.0002389516,0.9472347,0.00009637879,0.03885262,0.009557964,0.0008725506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001330992,0.05027381,0.9393026,0.0005410846,0.0009244505,0.0004798912,0.00001653129,0.0007724116,0.006358264],"genre_scores_gemma":[0.7656369,0.2180098,0.001877437,0.0001068776,0.002317623,0.000340281,0.0001548962,0.0002212301,0.01133495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9374251,"threshold_uncertainty_score":0.9996817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004327285517564418,"score_gpt":0.2219163668822553,"score_spread":0.2175890813646909,"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."}}