{"id":"W2050900560","doi":"10.1016/j.watres.2014.02.015","title":"Variance decomposition: A tool enabling strategic improvement of the precision of analytical recovery and concentration estimates associated with microorganism enumeration methods","year":2014,"lang":"en","type":"article","venue":"Water Research","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Water Network","keywords":"Variance (accounting); Enumeration; Statistics; Replication (statistics); Design of experiments; Seeding; Computer science; Mathematics; Biological system; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01114276,0.002079782,0.001730205,0.003407732,0.000855158,0.002478993,0.00142908,0.001194785,0.002794501],"category_scores_gemma":[0.03299705,0.0008699836,0.00168775,0.002486972,0.0008950189,0.001716708,0.002223263,0.002374902,0.001352281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000534737,"about_ca_system_score_gemma":0.002646426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0026632,"about_ca_topic_score_gemma":0.003204856,"domain_scores_codex":[0.9935489,0.00279105,0.0005769651,0.001150358,0.001673709,0.0002590686],"domain_scores_gemma":[0.9830661,0.01031304,0.001373019,0.002277601,0.002746649,0.0002236572],"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":[0.0006586338,0.0003115182,0.009963294,0.0006499691,0.0005892795,0.0001883452,0.0006076412,0.02476837,0.1505731,0.03102873,0.005547194,0.7751139],"study_design_scores_gemma":[0.0001294014,0.0006705371,0.01714269,0.0001884669,0.0004280209,0.0009549149,0.0002677314,0.6960727,0.2014146,0.05790121,0.02441783,0.0004119267],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002858003,0.00004197641,0.9958656,0.00003383346,0.00002076471,0.00002047896,0.0001020826,0.0008575168,0.0001998124],"genre_scores_gemma":[0.03528246,0.00009437487,0.963088,0.00005016879,0.00003106137,0.0001115785,0.0003693592,0.0005287444,0.0004443414],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01114276,"threshold_uncertainty_score":0.05892926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05619717407685357,"score_gpt":0.3720982302845523,"score_spread":0.3159010562076987,"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."}}