{"id":"W4289711940","doi":"10.7717/peerj-cs.1066","title":"Causal graph extraction from news: a comparative study of time-series causality learning techniques","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Agencia Nacional de Promoción Científica y Tecnológica; Universidad Nacional del Sur; Consejo Nacional de Investigaciones Científicas y Técnicas; Compute Canada","keywords":"Causal structure; Computer science; Causality (physics); Machine learning; Graph; Artificial intelligence; Time series; Causal inference; Event (particle physics); Natural language processing; Theoretical computer science; Data mining; Information retrieval; Data science; Econometrics; Mathematics","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":[0.005601887,0.00135953,0.0009359202,0.009900222,0.0006100981,0.001491026,0.001201277,0.001274558,0.001606019],"category_scores_gemma":[0.01963469,0.0003554676,0.002007668,0.007630145,0.0005498321,0.005006026,0.0008083508,0.001592143,0.0006272434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005814703,"about_ca_system_score_gemma":0.0008715699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002887189,"about_ca_topic_score_gemma":0.003244856,"domain_scores_codex":[0.9973332,0.001205347,0.000222519,0.0005523753,0.0005933405,0.00009319289],"domain_scores_gemma":[0.9755019,0.02063094,0.0008714209,0.001367285,0.001468774,0.0001597086],"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":[0.0003853018,0.0003932214,0.01045527,0.00125048,0.0007264141,0.0002412174,0.0006289789,0.07506984,0.002107911,0.01257986,0.004869125,0.8912925],"study_design_scores_gemma":[0.00008207815,0.0003007636,0.01407924,0.0002959098,0.0006181895,0.0004599783,0.0009485122,0.9199703,0.006751945,0.03274493,0.02364849,0.00009968274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08524394,0.02685912,0.8720256,0.001738441,0.0004158162,0.0003822185,0.001675218,0.002907768,0.00875186],"genre_scores_gemma":[0.4794555,0.02073034,0.4892915,0.0002607093,0.000864014,0.0002719239,0.006284485,0.0003447233,0.002496699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009900222,"threshold_uncertainty_score":0.02962595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04088231282822022,"score_gpt":0.3120596989749664,"score_spread":0.2711773861467461,"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."}}