{"id":"W2420030399","doi":"10.1021/acsami.5b01876","title":"Enhanced Radio Frequency Biosensor for Food Quality Detection Using Functionalized Carbon Nanofillers","year":2015,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; University of Toronto","funders":"","keywords":"Materials science; Carbon nanotube; Electrical conductor; Biosensor; Composite material; Composite number; Coating; Radio-frequency identification; Nanotechnology","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":[0.0001386933,0.0004969978,0.0002011856,0.0001961209,0.00009828301,0.0001934417,0.0004517219,0.0005961395,0.000303532],"category_scores_gemma":[0.0002027112,0.0001419304,0.0002219323,0.0001443862,0.000160856,0.0004066047,0.0001851009,0.0003621925,0.0002080384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003169233,"about_ca_system_score_gemma":0.0001499819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003430069,"about_ca_topic_score_gemma":0.0007402619,"domain_scores_codex":[0.9998139,0.00002331147,0.00001215227,0.00004703018,0.00008599842,0.00001757487],"domain_scores_gemma":[0.9999045,0.00001842999,0.00003031335,0.000005574215,0.00003062514,0.00001058325],"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":[0.000007965134,0.000004720918,0.00002281199,0.00001960566,0.000001180574,0.000009841951,0.000002685254,0.00004915825,0.9988741,0.00003823946,0.000006889877,0.0009626744],"study_design_scores_gemma":[0.000003040815,0.0001142444,0.0003742622,0.000002234438,0.000005448698,0.00008045357,0.000003450085,0.001749107,0.9963973,0.00002087564,0.00124468,0.000004836591],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.872328,0.004765406,0.1189594,0.0002497495,0.0001780433,0.0001307996,0.0001519799,0.0005446348,0.002692023],"genre_scores_gemma":[0.878756,0.001761967,0.1153855,0.0001667705,0.00004670769,0.00007301209,0.0002078997,0.00003980535,0.00356232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005961395,"threshold_uncertainty_score":0.002299428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04562719140296963,"score_gpt":0.2676448246698879,"score_spread":0.2220176332669183,"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."}}