{"id":"W2018795457","doi":"10.3758/bf03206555","title":"Fitting distributions using maximum likelihood: Methods and packages","year":2004,"lang":"en","type":"article","venue":"Behavior Research Methods, Instruments, & Computers","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":167,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Weibull distribution; Log-normal distribution; Gumbel distribution; Mathematics; Statistics; Estimator; Gaussian; Applied mathematics; Algorithm; Computer science; Extreme value theory","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":[0.01272376,0.002281049,0.002956723,0.003538614,0.001264232,0.003337757,0.00557928,0.00304648,0.02715645],"category_scores_gemma":[0.06467499,0.002925964,0.002798471,0.004526506,0.001383133,0.004267152,0.003519436,0.005032392,0.01091287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100285,"about_ca_system_score_gemma":0.002816503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002972988,"about_ca_topic_score_gemma":0.003212335,"domain_scores_codex":[0.9934383,0.004599303,0.0003309067,0.0005809375,0.0009057567,0.0001448919],"domain_scores_gemma":[0.9702533,0.02473683,0.0008370364,0.002472007,0.001443535,0.0002572792],"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":[0.000347127,0.0004477701,0.001578742,0.001442115,0.0007317618,0.0001874794,0.0008255671,0.1509037,0.00163327,0.2366015,0.06090335,0.5443976],"study_design_scores_gemma":[0.0001433125,0.00003653579,0.0005092445,0.0002084862,0.0001228836,0.0002225253,0.00007924847,0.5874226,0.002666636,0.3762646,0.03220186,0.0001220689],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002071902,0.0000636,0.9976026,0.00006387992,0.00001056306,0.00004401525,0.0001858477,0.001584569,0.0002377244],"genre_scores_gemma":[0.006212773,0.0001717023,0.9905434,0.00004785137,0.00002751993,0.0005331689,0.0004161729,0.001427835,0.0006196909],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02715645,"threshold_uncertainty_score":0.09084737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2979135059293795,"score_gpt":0.5754612622245866,"score_spread":0.2775477562952071,"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."}}