{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002310079,0.0003379495,0.0003100345,0.0002487511,0.0003497576,0.0008865631,0.00085507,0.001562358,0.003563428],"category_scores_gemma":[0.001013506,0.0001921839,0.0002234313,0.0002320543,0.0006159258,0.001461868,0.0006872663,0.0009096807,0.001694122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002033779,"about_ca_system_score_gemma":0.0002938714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002967714,"about_ca_topic_score_gemma":0.0004413665,"domain_scores_codex":[0.9998139,0.00001778936,0.000006358739,0.00005635071,0.00008214092,0.00002359402],"domain_scores_gemma":[0.9997602,0.00007396203,0.0000257137,0.00003669153,0.00005500886,0.00004832431],"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":[0.00009642795,0.0001338134,0.001106302,0.0002040235,0.00003150678,0.0003668732,0.0001992703,0.0007098684,0.9117174,0.009793825,0.003262708,0.07237806],"study_design_scores_gemma":[0.00007787685,0.001597775,0.009526997,0.0002011276,0.0001291695,0.002882052,0.0006579428,0.04539336,0.8272685,0.02372861,0.08840683,0.0001296385],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6484357,0.00556362,0.2470137,0.01162004,0.003922583,0.0002491114,0.0006269943,0.00387147,0.07869684],"genre_scores_gemma":[0.8638323,0.001967097,0.07108927,0.003473911,0.0002059999,0.00007966583,0.0002298963,0.0002176067,0.05890428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003563428,"threshold_uncertainty_score":0.01192087,"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."}}