{"id":"W2091786172","doi":"10.1021/es902382a","title":"Particle and Microorganism Enumeration Data: Enabling Quantitative Rigor and Judicious Interpretation","year":2010,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Enumeration; Replicate; Bayes' theorem; Statistics; Sampling (signal processing); Reduction (mathematics); Variance (accounting); Count data; Variance reduction; Computer science; Sample (material); Data reduction; Probabilistic logic; Sample size determination; Bayesian probability; Mathematics; Algorithm; Poisson distribution; Monte Carlo method; Chemistry","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":[0.03238613,0.001219417,0.00197257,0.005129804,0.0007404654,0.004309021,0.002256487,0.002489549,0.001487744],"category_scores_gemma":[0.1064288,0.00114738,0.001077465,0.004407574,0.003292199,0.005690012,0.003716053,0.002953951,0.0006612564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001300201,"about_ca_system_score_gemma":0.00250434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00214616,"about_ca_topic_score_gemma":0.00266311,"domain_scores_codex":[0.9791672,0.01175679,0.001322178,0.001859069,0.005692413,0.0002023347],"domain_scores_gemma":[0.9270762,0.04829281,0.008473167,0.01072819,0.00500621,0.0004235085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003745745,0.0002475349,0.03035399,0.002284842,0.0005260897,0.0003725924,0.001986076,0.09818746,0.04036237,0.3631047,0.006356993,0.4558426],"study_design_scores_gemma":[0.00006712761,0.0001830293,0.023106,0.0005099269,0.0001296385,0.00050829,0.0004855957,0.3647529,0.02452164,0.5619164,0.02350309,0.0003164305],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006344029,0.0002353198,0.9910789,0.0004646832,0.00003375997,0.00006847583,0.0006447664,0.0003275315,0.0008025564],"genre_scores_gemma":[0.1294134,0.0007664388,0.8672025,0.0003447545,0.0001300602,0.0003969613,0.001054497,0.0002201751,0.0004714036],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03238613,"threshold_uncertainty_score":0.1712763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02962140750926754,"score_gpt":0.3420014934498134,"score_spread":0.3123800859405458,"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."}}