{"id":"W3095140182","doi":"10.3390/s20216346","title":"Colocalized Sensing and Intelligent Computing in Micro-Sensors","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Division of Electrical, Communications and Cyber Systems","keywords":"Microelectromechanical systems; Waveform; SIGNAL (programming language); Electronic engineering; Noise (video); Computer science; Acceleration; Analog signal; Sampling (signal processing); Signal processing; Digital signal processing; Engineering; Electrical engineering; Voltage; Artificial intelligence; Physics; Telecommunications; Detector","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":[0.00022185,0.0001934657,0.0002902383,0.0002271796,0.0001801406,0.000531285,0.000575118,0.0004149185,0.001189652],"category_scores_gemma":[0.0005886487,0.0001324733,0.0001673545,0.000215849,0.0008856973,0.001136643,0.0007965003,0.0004316583,0.0002046764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003894518,"about_ca_system_score_gemma":0.0002107815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002167413,"about_ca_topic_score_gemma":0.0004240842,"domain_scores_codex":[0.999828,0.00003440292,0.000009092441,0.00005482242,0.00005357744,0.00002002805],"domain_scores_gemma":[0.9998127,0.00009056497,0.00002637283,0.00003990048,0.00001793396,0.00001271366],"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":[0.0002247735,0.0001059563,0.0009151401,0.0003677749,0.00006456263,0.0002558452,0.0002254689,0.05631231,0.5738789,0.2149883,0.0009448132,0.1517162],"study_design_scores_gemma":[0.00002446225,0.0003023209,0.0008550138,0.00002808765,0.00002683579,0.0002830763,0.00008747153,0.6658046,0.2705585,0.05247113,0.009510697,0.00004785932],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1826956,0.002757981,0.8042172,0.000459906,0.000129066,0.00005918675,0.00004745297,0.000729779,0.008903866],"genre_scores_gemma":[0.8753021,0.0004859911,0.1213648,0.000106553,0.00004325767,0.00004024778,0.00001999054,0.00003095103,0.002606115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001189652,"threshold_uncertainty_score":0.003979802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02375309538363849,"score_gpt":0.2451226231084133,"score_spread":0.2213695277247749,"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."}}