{"id":"W2080056247","doi":"10.1086/507773","title":"Markov Chain Monte Carlo Methods Applied to Photometric Spot Modeling","year":2006,"lang":"en","type":"article","venue":"Publications of the Astronomical Society of the Pacific","topic":"Stellar, planetary, and galactic studies","field":"Physics and Astronomy","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Markov chain Monte Carlo; Monte Carlo method; Markov chain; Statistical physics; Hybrid Monte Carlo; Monte Carlo molecular modeling; Markov chain mixing time; Markov model; Algorithm; Physics; Computer science; Markov property; Mathematics; Statistics; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0004731454,0.0001672341,0.0002973177,0.00005693616,0.0002662089,0.00003271668,0.0007463582,0.00004359375,0.00005383355],"category_scores_gemma":[0.00002495004,0.0001136368,0.0004868774,0.0007070078,0.0001481786,0.00004729751,0.0002979989,0.0001791103,0.000006175975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005357022,"about_ca_system_score_gemma":0.00004883145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006487835,"about_ca_topic_score_gemma":0.000003008089,"domain_scores_codex":[0.9987983,0.00007704374,0.0004071139,0.000251929,0.000187303,0.0002783575],"domain_scores_gemma":[0.9985851,0.0002525279,0.0002547797,0.000756281,0.00009515825,0.00005611885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001162089,0.0001533968,0.9390428,0.00001053091,0.0002120255,2.957564e-10,0.0003204454,0.04736425,0.0005689585,0.0016098,0.007618543,0.003087632],"study_design_scores_gemma":[0.0004234906,0.00001244776,0.9377093,0.00001403682,0.0001490005,1.53278e-7,0.00145888,0.05210088,0.003221058,0.001368663,0.003311707,0.0002303432],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9194588,0.0001552215,0.04914264,0.005680407,0.0002605207,0.0008322202,0.00009137504,0.0000308903,0.02434794],"genre_scores_gemma":[0.9662328,0.000001325446,0.03266083,0.00001483958,0.0001514163,0.00006153324,0.00001068475,0.00001470301,0.0008519104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04677397,"threshold_uncertainty_score":0.4633976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01579591296424551,"score_gpt":0.2491583264028202,"score_spread":0.2333624134385747,"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."}}