{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004528049,0.0003199471,0.0006512835,0.0001686291,0.0002980759,0.00005876756,0.000661328,0.0002987725,0.0001623821],"category_scores_gemma":[0.003164686,0.0002976646,0.0001108257,0.0001889648,0.0002330634,0.0002150517,0.0003074946,0.0003368555,0.000003882681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002058499,"about_ca_system_score_gemma":0.00006588268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003730048,"about_ca_topic_score_gemma":0.0001922608,"domain_scores_codex":[0.9967975,0.0006329273,0.0007726158,0.00071439,0.0001837258,0.00089886],"domain_scores_gemma":[0.9912397,0.007387988,0.0003215856,0.0007589567,0.00008974392,0.0002020658],"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":[0.001003749,0.001809767,0.005960266,0.0009693066,0.001108111,0.0005431237,0.001120019,0.002657473,0.03483361,0.3868732,0.02822324,0.5348982],"study_design_scores_gemma":[0.0004989768,0.0001062628,0.003455307,0.0000157217,0.0001291455,0.00003144376,0.00003828744,0.6269995,0.0000806746,0.3682418,0.0001514762,0.0002513857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01236014,0.00001107854,0.9836483,0.002019328,0.0004839417,0.00084078,0.0001941481,0.0001265284,0.0003157466],"genre_scores_gemma":[0.05065823,0.000001593023,0.9466133,0.002394573,0.0001782318,0.00003672202,0.00005852994,0.00004783748,0.00001100388],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.624342,"threshold_uncertainty_score":0.9999475,"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."}}