{"id":"W3198461221","doi":"10.1002/aelm.202100891","title":"Bio‐Inspired Adaptive Sensing through Electropolymerization of Organic Electrochemical Transistors","year":2021,"lang":"en","type":"preprint","venue":"Advanced Electronic Materials","topic":"Conducting polymers and applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"European Research Council","keywords":"PEDOT:PSS; Transconductance; Materials science; Microfabrication; Nanotechnology; Capacitance; Transistor; Optoelectronics; Dielectric spectroscopy; Raman spectroscopy; Voltage; Electrochemistry; Electrode; Electrical engineering; Chemistry; Layer (electronics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001054204,0.0003220421,0.0001200226,0.0001740776,0.00006350777,0.0002914748,0.0002061455,0.0002906797,0.0007184388],"category_scores_gemma":[0.0002430064,0.0001335402,0.0001296107,0.0001879475,0.0002384264,0.0002557212,0.0002306021,0.0003674901,0.0002183509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001434897,"about_ca_system_score_gemma":0.00007098528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001379353,"about_ca_topic_score_gemma":0.0002311474,"domain_scores_codex":[0.9999177,0.000009022993,0.000004012465,0.00002521138,0.00002728416,0.00001667607],"domain_scores_gemma":[0.999894,0.0000447422,0.00003062087,0.00001095651,0.00001081564,0.000008834569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007401621,0.000007731846,0.00002480634,0.00001981598,0.000001940318,0.00002322085,0.000007341899,0.0002149343,0.9970269,0.0001682883,0.00003765254,0.002460144],"study_design_scores_gemma":[0.000003865654,0.00002990827,0.0003216816,0.000002008315,0.000003389036,0.00004373107,0.000004591207,0.00294039,0.9948113,0.00006680387,0.001769044,0.000003286443],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9411651,0.002873615,0.04921753,0.0003666,0.0002238771,0.00004919798,0.0001406462,0.0005701003,0.005393263],"genre_scores_gemma":[0.9864339,0.001075704,0.009245356,0.0001032556,0.00004057232,0.00002350882,0.0000424919,0.00003543472,0.002999814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007184388,"threshold_uncertainty_score":0.002403438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01239197382480817,"score_gpt":0.2558195652786681,"score_spread":0.2434275914538599,"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."}}