{"id":"W2320263721","doi":"10.1021/ac503639s","title":"Aptamer-Based Label-Free Impedimetric Biosensor for Detection of Progesterone","year":2014,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":174,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Aptamer; Chemistry; Biosensor; Dissociation constant; Oligonucleotide; Circular dichroism; Dielectric spectroscopy; Biophysics; DNA; Combinatorial chemistry; Biochemistry; Molecular biology; Electrochemistry; Electrode; Receptor; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001983477,0.0001620538,0.000232671,0.00004652127,0.00004466471,0.00001080595,0.0001881884,0.0002274181,0.000001832909],"category_scores_gemma":[0.0007743122,0.0001435438,0.0001727685,0.0002388408,0.0001499241,0.000002249908,0.00005595342,0.00007617928,7.699866e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001480601,"about_ca_system_score_gemma":0.00002908982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003570506,"about_ca_topic_score_gemma":0.000002399944,"domain_scores_codex":[0.9989854,0.00001981522,0.0002712822,0.0003591925,0.0001413031,0.0002230039],"domain_scores_gemma":[0.9990003,0.00004969797,0.0001335289,0.0005360281,0.0001932787,0.00008715485],"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":[0.00009283894,0.0000968257,0.0002074689,0.00008188056,0.00005134878,2.457603e-7,5.527535e-7,0.000003349624,0.9938816,0.00001264301,0.0001816908,0.005389598],"study_design_scores_gemma":[0.0006151943,0.0002910761,0.00006013024,0.00001303151,0.000106919,0.000002850673,0.000006088063,0.004103689,0.9892688,0.0001006891,0.005254584,0.0001768809],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9506118,0.00007917768,0.04815332,0.0001870483,0.00002279548,0.000153706,0.00004003191,0.00005223828,0.0006999021],"genre_scores_gemma":[0.9918261,0.00001020159,0.0073514,0.0001092284,0.0001934658,0.00001251936,0.00008700991,0.00002004432,0.0003900702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04121428,"threshold_uncertainty_score":0.5853548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132629512761064,"score_gpt":0.272945189368726,"score_spread":0.2616188942411154,"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."}}