{"id":"W2328486433","doi":"10.1097/hp.0b013e31825ff079","title":"Multichannel Statistical Analysis of Low-level Radioactivity in the Presence of Background Counts","year":2012,"lang":"en","type":"article","venue":"Health Physics","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Canada","funders":"","keywords":"Poisson distribution; Statistics; Bayesian probability; Mathematics; Statistical analysis; Probability distribution; Count data; Statistical model; Gamma distribution","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":[0.002879984,0.0004251844,0.0007884311,0.001172942,0.0003139536,0.0009142586,0.0008072101,0.0004185828,0.001003746],"category_scores_gemma":[0.008899066,0.0002836775,0.0004990619,0.0008709726,0.0006440271,0.001015082,0.0007469887,0.0006152994,0.0002876373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005770667,"about_ca_system_score_gemma":0.0008542032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001337161,"about_ca_topic_score_gemma":0.002004534,"domain_scores_codex":[0.9984928,0.0003754259,0.00005485154,0.0003826767,0.0005954587,0.00009861994],"domain_scores_gemma":[0.995724,0.002672993,0.0005410278,0.0004460231,0.0005403719,0.00007561598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008938782,0.0002892729,0.03960832,0.0006161716,0.0005190942,0.0007583422,0.0005695716,0.2183878,0.1320102,0.05755816,0.002073624,0.5467156],"study_design_scores_gemma":[0.00002068616,0.0002432186,0.02058705,0.00003663161,0.0001304677,0.0010572,0.0001035151,0.8899848,0.05417988,0.02795568,0.005571541,0.0001292805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05095337,0.0003106621,0.9471661,0.00004931164,0.00003726251,0.00002720818,0.0001516439,0.0004162145,0.0008882061],"genre_scores_gemma":[0.5398018,0.0006667972,0.455775,0.0001566031,0.0001548879,0.0001836174,0.0005462402,0.0002733422,0.002441705],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002879984,"threshold_uncertainty_score":0.01523095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05939848727688657,"score_gpt":0.3026618367586088,"score_spread":0.2432633494817222,"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."}}