{"id":"W1525398541","doi":"","title":"Argument Mining by Applying Argumentation Schemes","year":2012,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Argumentation theory; Argument (complex analysis); Informal logic; Argument map; Identification (biology); Computer science; Artificial intelligence; Epistemology; Data science; Work (physics); Natural language processing; Mathematics education; Psychology; Engineering; Philosophy","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":[0.01164781,0.001617636,0.0013959,0.01215083,0.002125869,0.005632762,0.004040694,0.002426743,0.009530198],"category_scores_gemma":[0.05453677,0.000941446,0.003383351,0.006165251,0.001987151,0.01000196,0.005949941,0.002576567,0.003053074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001164121,"about_ca_system_score_gemma":0.002083228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007065638,"about_ca_topic_score_gemma":0.0009906768,"domain_scores_codex":[0.9821658,0.009184378,0.001900621,0.001961403,0.004394899,0.0003929192],"domain_scores_gemma":[0.968156,0.02327004,0.001579275,0.004333727,0.002281479,0.0003793915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001876634,0.0003365446,0.003827248,0.001402305,0.0003257516,0.0003819668,0.002387239,0.02291807,0.004023963,0.2374467,0.006213587,0.720549],"study_design_scores_gemma":[0.0001502847,0.0001651179,0.001258564,0.0009410563,0.0002100169,0.0007278768,0.001107117,0.2987324,0.01254769,0.5868728,0.09718556,0.0001016093],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007544701,0.0004621346,0.9840363,0.0005174527,0.00007887566,0.0005989193,0.0003649291,0.001159745,0.005236891],"genre_scores_gemma":[0.06496281,0.000345876,0.9315327,0.00008221459,0.00005301287,0.0003835817,0.0007191732,0.0001362983,0.001784249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01215083,"threshold_uncertainty_score":0.06160021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01300762855715099,"score_gpt":0.2541010872852814,"score_spread":0.2410934587281304,"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."}}