{"id":"W3118252450","doi":"10.1038/s41928-020-00526-0","title":"A biosensor that learns on the go","year":2021,"lang":"en","type":"article","venue":"Nature Electronics","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Biosensor; Computer science; Nanotechnology; Chemistry; Materials science","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":[0.00006589203,0.0001293729,0.0001031246,0.00004552826,0.0001203994,0.00003466971,0.0001056425,0.0001517285,0.00006357948],"category_scores_gemma":[0.00005722104,0.00009303701,0.00008619138,0.0004137979,0.0000167185,0.00003367268,0.00001285357,0.001057263,0.000014334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006658254,"about_ca_system_score_gemma":0.00002933497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.910993e-7,"about_ca_topic_score_gemma":0.00003342644,"domain_scores_codex":[0.9992589,0.00002706904,0.00006881759,0.0001403529,0.0001728849,0.0003319984],"domain_scores_gemma":[0.9995614,0.0001236979,0.00001405518,0.0002306368,0.00004372134,0.00002648828],"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":[0.00004513851,0.0001539199,0.0005834632,0.00006357737,0.001137735,0.00003756285,0.000657359,0.00117745,0.08129012,0.3609095,0.4596533,0.0942909],"study_design_scores_gemma":[0.0001507036,0.00004354561,0.002815004,0.00001142275,0.00001143778,0.00001001203,0.00007887182,0.0003917254,0.1465383,0.0009302559,0.848846,0.0001727786],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6487476,0.07698688,0.0005072354,0.02723072,0.001565907,0.0005227001,0.00001754128,0.001620038,0.2428014],"genre_scores_gemma":[0.9953033,0.002351759,0.00004513141,0.001488441,0.0001019432,0.00001303092,0.000007562281,0.00002468037,0.0006641134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3891927,"threshold_uncertainty_score":0.459334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008603857724457544,"score_gpt":0.2126279588882349,"score_spread":0.2040241011637773,"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."}}