{"id":"W2921860240","doi":"10.1016/b978-0-12-814505-0.00003-5","title":"Nanostructured Materials for RFID Sensors","year":2019,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Conducting polymers and applications","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Radio-frequency identification; Flexibility (engineering); Identification (biology); Computer science; Bar (unit); Embedded system; Tracking (education); Computer hardware; Engineering; Computer architecture; Operating system; Physics; Mathematics","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002775553,0.0003982931,0.0005880285,0.00008000992,0.0001759638,0.0001289729,0.0003746142,0.0003356306,0.002478439],"category_scores_gemma":[0.00002805585,0.0003571212,0.0001866512,0.000006971947,0.0001292439,0.0000325163,0.0001155505,0.0001259578,0.0009397442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006628494,"about_ca_system_score_gemma":0.0001930944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001280986,"about_ca_topic_score_gemma":0.000005825892,"domain_scores_codex":[0.9982512,0.00001688001,0.0004801002,0.0006218269,0.0002852493,0.0003447489],"domain_scores_gemma":[0.9985019,0.00007936089,0.0004018331,0.0007707344,0.0001564715,0.00008970661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001978044,0.000002544223,1.122828e-7,0.0001236921,0.00002449794,0.00000148318,0.000109669,3.128132e-7,0.8178915,0.01230956,0.001278339,0.1682385],"study_design_scores_gemma":[0.0001627366,0.00002559546,8.415064e-7,0.00009317925,0.00006893157,0.00001002607,0.000005471476,2.225922e-7,0.3208545,0.006361321,0.6721079,0.0003093488],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01586992,0.0002910542,0.000004927491,0.0001471364,0.003291729,0.001739989,0.001270671,0.0001512628,0.9772333],"genre_scores_gemma":[0.00472232,0.00001057899,0.0008831082,0.0002321757,0.0006817634,0.0001001672,0.00008010415,0.0001233072,0.9931664],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6708295,"threshold_uncertainty_score":0.9998881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0199287288359513,"score_gpt":0.2539036622253344,"score_spread":0.2339749333893831,"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."}}