{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003098519,0.0002223697,0.000198267,0.00005547372,0.0002033406,0.0001941077,0.0008300689,0.0000849401,0.00005428757],"category_scores_gemma":[0.00002358951,0.0001457643,0.00008140384,0.0002454096,0.00003877988,0.0007705603,0.0001296266,0.0001226738,0.0001134949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006484038,"about_ca_system_score_gemma":0.0000904255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003379277,"about_ca_topic_score_gemma":0.000006009712,"domain_scores_codex":[0.9981069,0.00006390987,0.0002822395,0.0005830221,0.0003907858,0.0005731568],"domain_scores_gemma":[0.9987503,0.0002896774,0.00008197245,0.0006868136,0.00007243332,0.0001187935],"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":[0.00001671764,0.00004328751,0.00001917921,0.000004359992,0.00001333042,0.000003381057,0.00009149993,0.4459913,0.0001637139,0.5057792,0.0004298225,0.0474442],"study_design_scores_gemma":[0.0004906137,0.0000748345,0.00001012043,0.0002069323,0.000004143898,0.000001911168,0.000001573813,0.6958439,0.0002145101,0.3028561,0.00006973363,0.0002256286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000547745,0.00003627287,0.991602,0.001471696,0.0003646607,0.0001322138,0.000001136366,0.0004061673,0.005438088],"genre_scores_gemma":[0.8074059,0.00000406045,0.1904481,0.001763949,0.0001065504,0.00001614822,7.118613e-7,0.00001401467,0.0002405166],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8068582,"threshold_uncertainty_score":0.5944094,"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."}}