{"id":"W2111418884","doi":"10.1111/j.1461-0248.2007.01047.x","title":"Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods","year":2007,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":269,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Alberta","funders":"","keywords":"Markov chain Monte Carlo; Frequentist inference; Computer science; Bayesian probability; Algorithm; Likelihood function; Data mining; Bayesian inference; Machine learning; Statistics; Mathematics; Estimation theory; 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":[0.01202212,0.001627202,0.001962082,0.00325906,0.001073106,0.002294169,0.004162353,0.002031232,0.0047279],"category_scores_gemma":[0.06542285,0.001664481,0.002175178,0.003400324,0.002148897,0.003827284,0.004610206,0.004929513,0.002034051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105344,"about_ca_system_score_gemma":0.002384596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00263356,"about_ca_topic_score_gemma":0.002467593,"domain_scores_codex":[0.9930444,0.004070893,0.0004062956,0.0007402872,0.001579434,0.0001586342],"domain_scores_gemma":[0.9641103,0.02747239,0.001748147,0.004271788,0.002009702,0.000387799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008555675,0.0001423592,0.003318239,0.0005164103,0.0003062117,0.0002818876,0.0004995012,0.2343269,0.003818395,0.4472432,0.008548285,0.300913],"study_design_scores_gemma":[0.00003907825,0.00002366236,0.0003439153,0.00005593576,0.00002597164,0.0001007271,0.00001815373,0.7383747,0.001978677,0.2501758,0.00879467,0.0000687272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002403184,0.00002677237,0.9994003,0.00003189276,0.00001156495,0.00001165227,0.00002710821,0.0001711988,0.00007919918],"genre_scores_gemma":[0.01114679,0.00009709757,0.9876568,0.00007231965,0.00004511506,0.0002578906,0.000157992,0.0003034854,0.0002624897],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01202212,"threshold_uncertainty_score":0.0635798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1971455189971549,"score_gpt":0.4442246778074805,"score_spread":0.2470791588103257,"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."}}