{"id":"W2567674080","doi":"","title":"A Study of Approximate Inference in Probabilistic Relational Models","year":2010,"lang":"en","type":"article","venue":"Asian Conference on Machine Learning","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Computer science; Probabilistic logic; Theoretical computer science; 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":[0.01939833,0.001163285,0.004684797,0.002690519,0.002236017,0.005870698,0.008420072,0.003799324,0.008130421],"category_scores_gemma":[0.1872933,0.00257661,0.003126636,0.006096276,0.005850784,0.02210693,0.005364334,0.006963001,0.0004993708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004887984,"about_ca_system_score_gemma":0.002168136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01326048,"about_ca_topic_score_gemma":0.007886737,"domain_scores_codex":[0.9889116,0.00670851,0.0005798956,0.001561382,0.001692375,0.0005461722],"domain_scores_gemma":[0.6733725,0.3093925,0.00467864,0.007793651,0.003486807,0.001275917],"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":[0.0001967766,0.0001328986,0.002769363,0.0003332281,0.0002594168,0.0001662644,0.0008210902,0.2135389,0.0002200383,0.7546481,0.002764138,0.02414975],"study_design_scores_gemma":[0.00001825844,0.00002252602,0.0002608563,0.00003423411,0.00004207404,0.00004636587,0.00005669185,0.6137468,0.00009456138,0.3850307,0.0006304594,0.00001657569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04481698,0.003312591,0.9414977,0.004443885,0.0001128323,0.00007233321,0.0003302999,0.0002132701,0.00520009],"genre_scores_gemma":[0.7439203,0.004153771,0.2388472,0.001593154,0.001366629,0.0004003906,0.001335101,0.0004316034,0.007951832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01939833,"threshold_uncertainty_score":0.1025894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03307190487762339,"score_gpt":0.3031102335139692,"score_spread":0.2700383286363457,"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."}}