{"id":"W7095792784","doi":"","title":"CANADA S4S 0A2Expectation Propagation in ExGen Graphs for Summarization:","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Generalization; Heuristics; Node (physics); Domain (mathematical analysis); Process (computing)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002259588,0.0006930738,0.0004115065,0.002878644,0.001001325,0.001735485,0.0008262702,0.0007040261,0.005791685],"category_scores_gemma":[0.008705576,0.0003232922,0.0005830172,0.002580027,0.0006026725,0.00219265,0.001232274,0.0008275337,0.001452728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002141453,"about_ca_system_score_gemma":0.002355709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04179753,"about_ca_topic_score_gemma":0.06746149,"domain_scores_codex":[0.9985286,0.0006301163,0.00009163326,0.0003121013,0.0003733266,0.00006420845],"domain_scores_gemma":[0.9939743,0.002359236,0.0005288031,0.001273555,0.001688341,0.0001757566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003779768,0.0001409941,0.01302519,0.0004821958,0.0001197874,0.0004263955,0.001035409,0.1700885,0.01790537,0.05127104,0.05474995,0.6903772],"study_design_scores_gemma":[0.00006577185,0.00009884019,0.00557835,0.00007091436,0.0000530381,0.0002382806,0.000447081,0.8587878,0.02053658,0.05124252,0.06281081,0.00007001858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03670214,0.0002665175,0.9369753,0.001422847,0.00005773836,0.0002627195,0.004029092,0.01210646,0.008177186],"genre_scores_gemma":[0.2285359,0.0003470617,0.7535647,0.0002998352,0.00004975141,0.0002105275,0.008824631,0.001064373,0.007103315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04179753,"threshold_uncertainty_score":0.08310848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1197399713154023,"score_gpt":0.3703554875670823,"score_spread":0.25061551625168,"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."}}