{"id":"W2042034241","doi":"10.1016/j.mimet.2005.02.021","title":"Optimization of microbial DNA extraction and purification from raw wastewater samples for downstream pathogen detection by microarrays","year":2005,"lang":"en","type":"article","venue":"Journal of Microbiological Methods","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":102,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Institut National de la Recherche Scientifique; Université du Québec à Rimouski; Université de Montréal; Biotechnology Research Institute","funders":"Université de Montréal; F. Hoffmann-La Roche; Water Research Foundation; Water Environment Research Foundation","keywords":"DNA extraction; DNA microarray; Polymerase chain reaction; DNA; Biology; Extraction (chemistry); Wastewater; Chromatography; Microarray; Computational biology; Microbiology; Chemistry; Gene; Genetics; Environmental science; Gene expression; Environmental engineering","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":[0.0006043227,0.0001267192,0.0002138283,0.00006207846,0.00005986521,0.0000200515,0.0001158613,0.0002645852,0.00002558528],"category_scores_gemma":[0.0001717305,0.00009565283,0.0001233443,0.00005538591,0.00006995865,0.0000148236,0.00002496597,0.00009041756,3.455345e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002586345,"about_ca_system_score_gemma":0.00002346102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000330343,"about_ca_topic_score_gemma":0.000001737983,"domain_scores_codex":[0.9988071,0.0002968611,0.0005006462,0.0002320734,0.0000452715,0.0001180569],"domain_scores_gemma":[0.9989616,0.00006625513,0.0005831227,0.0001352476,0.00019812,0.00005565391],"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":[0.0004073237,0.00008392205,0.00005429875,0.000007393333,0.00003406841,4.54721e-8,0.00002901986,0.0006154622,0.9351672,0.000001436594,0.0008105387,0.06278933],"study_design_scores_gemma":[0.0007031897,0.0003399536,0.0003603936,0.00001358016,0.00004489649,0.00002953928,0.00007420455,0.000168279,0.9513569,0.00004064571,0.04676459,0.0001038682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4518185,0.001043043,0.5466569,0.0001662128,0.0001361075,0.0001226367,0.00004682873,0.000002858267,0.000006954858],"genre_scores_gemma":[0.605832,0.0006674287,0.3929434,0.00006754572,0.0002551854,0.000008907317,0.0001780682,0.000009882433,0.00003759219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1540136,"threshold_uncertainty_score":0.3900609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0222455777928559,"score_gpt":0.314380363703783,"score_spread":0.2921347859109271,"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."}}