{"id":"W4234277357","doi":"10.1007/978-3-540-77587-4_323","title":"Denaturing Gradient Gel Electrophoresis (DGGE) for Microbial Community Analysis","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Temperature gradient gel electrophoresis; Amplicon; Troubleshooting; Biology; Amplicon sequencing; Fingerprint (computing); Metagenomics; 16S ribosomal RNA; Chromatography; Polymerase chain reaction; Bacteria; Chemistry; Computer science; Genetics; Gene; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006989304,0.001706154,0.00162316,0.001771004,0.0004842881,0.001101054,0.002011442,0.0009987192,0.02077595],"category_scores_gemma":[0.0005884598,0.0009519735,0.0006444625,0.002914895,0.0003577544,0.001596749,0.001110205,0.002369325,0.03039324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004702866,"about_ca_system_score_gemma":0.0004883273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005833447,"about_ca_topic_score_gemma":0.001877912,"domain_scores_codex":[0.9994469,0.00007733178,0.00003349286,0.0001416353,0.0002700221,0.00003059848],"domain_scores_gemma":[0.9997513,0.000102055,0.00001507225,0.00004365141,0.00006690966,0.0000212012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000692922,0.0001252363,0.0002177081,0.001996601,0.00004395685,0.0001849645,0.0001732783,0.000744531,0.3715369,0.007827473,0.05849707,0.5585831],"study_design_scores_gemma":[0.00001362635,0.0001204953,0.001064631,0.000382803,0.00004657757,0.001253374,0.0000625894,0.00215271,0.1522244,0.01135228,0.8312494,0.00007711737],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004190621,0.09503086,0.8222856,0.0009321905,0.003294829,0.0004212161,0.003440486,0.006718477,0.0636858],"genre_scores_gemma":[0.008796563,0.06793302,0.7664211,0.0009715315,0.0004091566,0.0006752331,0.008045417,0.002151841,0.1445961],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02077595,"threshold_uncertainty_score":0.06950247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01408890891179538,"score_gpt":0.221329530300874,"score_spread":0.2072406213890787,"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."}}