{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001702979,0.0000556704,0.0000894795,0.0001380792,0.00007397859,0.0001107028,0.0001909236,0.0000212172,0.0003062812],"category_scores_gemma":[0.001890779,0.0000439494,0.00002081444,0.0006381971,0.00001321841,0.0003384967,0.00001836764,0.00002224594,0.00001605905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006387188,"about_ca_system_score_gemma":0.0002450114,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1028145,"about_ca_topic_score_gemma":0.8525884,"domain_scores_codex":[0.9985519,0.0001318251,0.0003735323,0.0002447608,0.0005742706,0.0001237154],"domain_scores_gemma":[0.9991778,0.0003185557,0.0000893633,0.0002356993,0.000143526,0.00003503334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001059468,0.00003425087,0.003948641,0.000007337837,0.000004305093,0.000001046803,0.0002038983,0.0004286715,0.0000431017,0.8638519,0.117912,0.01355428],"study_design_scores_gemma":[0.0008858441,0.00005087322,0.01601893,0.000007513402,0.000007218427,6.438591e-7,0.005240486,0.005326872,0.002149115,0.2290418,0.7410266,0.0002441425],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05628535,0.00005821246,0.8123597,0.00661639,0.001207071,0.002245196,0.00009653904,0.00005709759,0.1210745],"genre_scores_gemma":[0.98692,0.000003188047,0.003857251,0.001096287,0.00001193173,0.00007660123,0.00008466524,0.00000415326,0.007945953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9306346,"threshold_uncertainty_score":0.90316,"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."}}