{"id":"W1826695761","doi":"10.14864/softscis.2006.0.1866.0","title":"Sequential Monte Carlo for Marginal Optimisation Problems","year":2006,"lang":"en","type":"article","venue":"SCIS & ISIS SCIS & ISIS 2006","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Monte Carlo method; Markov chain Monte Carlo; Computer science; Econometrics; Mathematics; Statistics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001142177,0.000401255,0.0005388775,0.0002830429,0.0003492487,0.0003380963,0.0003656355,0.000237149,0.001150799],"category_scores_gemma":[0.0004022468,0.0003415808,0.0003818754,0.0004826825,0.0001203224,0.0005420746,0.0000616096,0.0001891262,0.0002026749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002227932,"about_ca_system_score_gemma":0.00008025608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003387405,"about_ca_topic_score_gemma":0.0004530901,"domain_scores_codex":[0.9967918,0.0001015605,0.001069494,0.0006141459,0.0008391329,0.0005838051],"domain_scores_gemma":[0.9980021,0.0003284344,0.0004355583,0.0005755096,0.0005236191,0.0001347921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001177815,0.0009553952,0.0002074738,0.0007187133,0.0001146026,0.000002858965,0.0009611476,0.001036042,0.003756383,0.5720519,0.4129906,0.007087091],"study_design_scores_gemma":[0.001747533,0.0002030127,0.0002082204,0.0002292169,0.0002461237,0.00001894446,0.0003363213,0.1007688,0.009323992,0.8494878,0.03658298,0.0008470585],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2999762,0.0004478033,0.6121134,0.007652787,0.0011057,0.005839211,0.0004635968,0.001093005,0.07130834],"genre_scores_gemma":[0.5587603,0.00002611288,0.4143218,0.0004652781,0.001094134,0.001013024,0.0002426146,0.000172018,0.0239047],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3764077,"threshold_uncertainty_score":0.9999036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05286125478927588,"score_gpt":0.3107961233700159,"score_spread":0.25793486858074,"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."}}