{"id":"W4382201698","doi":"10.1145/3582768.3582774","title":"Extracting Source Information From News Articles","year":2022,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Credibility; Recall; Variety (cybernetics); Information source (mathematics); Identification (biology); Information retrieval; Attribution; Precision and recall; Software; Data science; Source document; News media; World Wide Web; Artificial intelligence; Political science; Psychology","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":[0.003745798,0.00188198,0.001266534,0.04211175,0.001519894,0.004766589,0.001337561,0.001496774,0.004610929],"category_scores_gemma":[0.02620399,0.0009840182,0.001322801,0.02334873,0.0004663295,0.004851433,0.002428337,0.001710352,0.005750399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008974248,"about_ca_system_score_gemma":0.002105531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003929414,"about_ca_topic_score_gemma":0.004381378,"domain_scores_codex":[0.9963505,0.0005098227,0.0005247286,0.00067042,0.001707644,0.000236808],"domain_scores_gemma":[0.9719559,0.01395039,0.002408771,0.002098309,0.009099893,0.0004867882],"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":[0.0006710256,0.0003162902,0.04292688,0.005343756,0.0004646578,0.002492572,0.003544717,0.004368033,0.02311673,0.01012208,0.06854834,0.8380849],"study_design_scores_gemma":[0.0002725796,0.0004633712,0.1574263,0.003310876,0.00234037,0.005508673,0.00889838,0.1612446,0.0871701,0.0693652,0.5033998,0.0005997761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1746553,0.01582694,0.5614722,0.002777483,0.001565483,0.003083392,0.169821,0.02544436,0.04535385],"genre_scores_gemma":[0.2794905,0.008946003,0.5088333,0.0002607486,0.002118815,0.001546298,0.1861605,0.002116631,0.01052708],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04211175,"threshold_uncertainty_score":0.0198099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02317384518306734,"score_gpt":0.2196091479832862,"score_spread":0.1964353028002189,"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."}}