{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006106313,0.0004450451,0.0003379678,0.000390304,0.0002167806,0.0005325604,0.0005898055,0.001091026,0.0007805642],"category_scores_gemma":[0.0006393511,0.0003184352,0.0002341532,0.000289773,0.000420141,0.0003639905,0.000369049,0.0006478183,0.0005847422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000460305,"about_ca_system_score_gemma":0.0002056471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005328783,"about_ca_topic_score_gemma":0.000842198,"domain_scores_codex":[0.9990829,0.0002052377,0.00003424875,0.0001790293,0.0004090309,0.00008946355],"domain_scores_gemma":[0.9996624,0.0001224133,0.00005963514,0.00002922369,0.00009091305,0.00003549717],"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":[0.000008537181,0.000008275276,0.00006772179,0.00002224307,0.000001445556,0.00001488248,0.00001321548,0.00005902565,0.9981915,0.00008446918,0.00003073219,0.00149798],"study_design_scores_gemma":[0.000002617258,0.00005715426,0.0004474111,0.0000031739,0.000004477701,0.0001160641,0.00001478774,0.001731962,0.9961449,0.00007237071,0.001398934,0.000006129503],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7215843,0.007596667,0.2635085,0.0008649427,0.0002860904,0.0001506573,0.0002174572,0.0009177619,0.004873621],"genre_scores_gemma":[0.9117615,0.002762277,0.0804932,0.0002609291,0.00003074784,0.00007352147,0.0001788936,0.00003806387,0.004400824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001091026,"threshold_uncertainty_score":0.003339827,"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."}}