{"id":"W4386646549","doi":"10.1002/ange.202312407","title":"Adaptive Supramolecular Networks: Emergent Sensing from Complex Systems","year":2023,"lang":"en","type":"article","venue":"Angewandte Chemie","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Analyte; Supramolecular chemistry; Biological system; Molecular recognition; Computer science; Nanotechnology; Materials science; Chemistry; Molecule; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001819621,0.000145051,0.0001356057,0.0001719343,0.0001384001,0.0003958919,0.000229335,0.0002860648,0.0008243505],"category_scores_gemma":[0.0004198488,0.0001275256,0.00007550863,0.0001030465,0.0005097239,0.0006745662,0.0004593026,0.0004402217,0.0001378304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002435183,"about_ca_system_score_gemma":0.0001004836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008689135,"about_ca_topic_score_gemma":0.0001187632,"domain_scores_codex":[0.999904,0.0000237464,0.000003500577,0.00002487861,0.00003145839,0.00001238853],"domain_scores_gemma":[0.9998666,0.00004991773,0.00002771587,0.00001845398,0.00001476594,0.0000224418],"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":[0.00007206912,0.00008732465,0.0009159058,0.0001452904,0.00002282194,0.0003381743,0.0002429377,0.02228641,0.8730285,0.06938781,0.0009710543,0.03250183],"study_design_scores_gemma":[0.00009773589,0.0003167414,0.004539929,0.0000360205,0.00002410533,0.0006291324,0.0001794131,0.4576729,0.374857,0.1392272,0.02236098,0.00005889084],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7588193,0.001354295,0.2270169,0.0007226131,0.0001098442,0.00009483794,0.00009906083,0.0005948442,0.01118832],"genre_scores_gemma":[0.9849341,0.000209438,0.01381755,0.00009712622,0.00001944246,0.00003926732,0.00002691489,0.00001332282,0.0008427773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008243505,"threshold_uncertainty_score":0.002757788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02808489026849388,"score_gpt":0.2729855868427509,"score_spread":0.244900696574257,"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."}}