{"id":"W2469689378","doi":"10.1145/2911451.2914750","title":"Ranking Documents Through Stochastic Sampling on Bayesian Network-based Models","year":2016,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ranking (information retrieval); Weighting; Computer science; Inference; Data mining; Bayesian network; Bayesian probability; Bipartite graph; Set (abstract data type); Sampling (signal processing); Posterior probability; Bayesian inference; Machine learning; Artificial intelligence; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006019691,0.001157928,0.002491649,0.00374988,0.001029378,0.002825508,0.002588299,0.001802115,0.003402778],"category_scores_gemma":[0.02830083,0.001180084,0.001326647,0.003945724,0.001496117,0.004754577,0.001331873,0.002112474,0.001040845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002603433,"about_ca_system_score_gemma":0.001285536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01480264,"about_ca_topic_score_gemma":0.0180859,"domain_scores_codex":[0.9963046,0.002123659,0.0001318822,0.000491289,0.000775912,0.0001726517],"domain_scores_gemma":[0.9831492,0.01442184,0.0007843499,0.0005753658,0.0008695809,0.0001996905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001881876,0.0001016697,0.0013696,0.0001544735,0.0001089247,0.0001115425,0.0001675643,0.8481272,0.0007856632,0.0915783,0.001880955,0.05542588],"study_design_scores_gemma":[0.00001207084,0.00001121818,0.0001191265,0.00000906797,0.00001007165,0.00001463487,0.000007514873,0.9682572,0.0001174199,0.03112464,0.0003083113,0.000008751975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01425998,0.0004735551,0.9832646,0.0003209875,0.00002606221,0.00008886697,0.0001699441,0.0002679029,0.001128051],"genre_scores_gemma":[0.5508928,0.002246444,0.4347918,0.0004268274,0.0004527783,0.0007473649,0.001699025,0.0002063646,0.008536731],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01480264,"threshold_uncertainty_score":0.03183556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0610169651294233,"score_gpt":0.2901062861407753,"score_spread":0.229089321011352,"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."}}