{"id":"W2065417035","doi":"10.1021/ac901445b","title":"Affinity Sensing for Transgenes Detection in Antidoping Control","year":2009,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Università degli Studi di Firenze; Ministero della Salute; World Anti-Doping Agency","keywords":"Oligonucleotide; Chemistry; Transgene; Gene; Plasmid; DNA; Computational biology; Molecular biology; Biosensor; Green fluorescent protein; Genetic enhancement; Biochemistry; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009264807,0.0001017283,0.0001220575,0.00001469748,0.00002947683,0.00001277046,0.000055084,0.00012418,0.000004299732],"category_scores_gemma":[0.00008380176,0.0001098247,0.00008999564,0.00006252729,0.00001791381,0.000001637135,0.000005490096,0.00006810543,5.94751e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001097453,"about_ca_system_score_gemma":0.00001616651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003629926,"about_ca_topic_score_gemma":0.00001002252,"domain_scores_codex":[0.9993697,0.00000609786,0.0001507771,0.0002178775,0.0000524112,0.0002030888],"domain_scores_gemma":[0.9997462,0.00001544309,0.00001703221,0.0001346648,0.00003073183,0.00005593946],"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":[0.00005446959,0.00002008567,0.0001785146,0.00002610625,0.00001254437,0.000001848181,0.000005499628,0.001066453,0.9918047,0.000003965849,0.0000315602,0.006794257],"study_design_scores_gemma":[0.0007075882,0.00005682011,0.001895948,0.00001323642,0.00002057773,0.000008315642,0.00001828385,0.02052413,0.9753144,0.0000680426,0.001223916,0.0001487585],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8224114,0.0001841754,0.176496,0.0001497148,0.00003255353,0.0000836641,0.000004275686,0.00001297188,0.0006252672],"genre_scores_gemma":[0.9990616,0.00002080845,0.0004664994,0.0001190878,0.0002201437,0.000002092975,0.00001526592,0.000008316619,0.00008622972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1766502,"threshold_uncertainty_score":0.4478521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007890126770130379,"score_gpt":0.295621216801272,"score_spread":0.2877310900311416,"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."}}