{"id":"W2729052508","doi":"10.1111/coin.12120","title":"From French Wikipedia to Erudit: A test case for cross‐domain open information extraction","year":2017,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Classifier (UML); Pipeline (software); Information extraction; Entity linking; Information retrieval; Open domain; Domain (mathematical analysis); Natural language processing; Task (project management); Artificial intelligence; Named-entity recognition; Precision and recall; Question answering; Knowledge base; Mathematics; Programming language","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":[0.005179347,0.001059736,0.0008039875,0.004647454,0.002026977,0.0026171,0.001130447,0.002621264,0.003104848],"category_scores_gemma":[0.02441605,0.0003002749,0.0007738206,0.002988149,0.001099156,0.003839165,0.003929039,0.00131667,0.002498297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071378,"about_ca_system_score_gemma":0.001199767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01360348,"about_ca_topic_score_gemma":0.01319068,"domain_scores_codex":[0.9934592,0.002866832,0.0005824695,0.001138053,0.001525026,0.0004285019],"domain_scores_gemma":[0.9760175,0.01562128,0.0006903685,0.003413766,0.003708995,0.0005480279],"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":[0.002208109,0.002215024,0.06954047,0.003492577,0.0006190834,0.02310375,0.01373319,0.01820796,0.03309835,0.01431961,0.1309067,0.6885552],"study_design_scores_gemma":[0.0006717235,0.001356798,0.06237429,0.0009468114,0.0004530386,0.01680407,0.01284932,0.2148727,0.1566287,0.02302644,0.5096102,0.0004058124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8236721,0.003880353,0.09348077,0.005308135,0.0008429526,0.0008970392,0.01704019,0.02640305,0.02847544],"genre_scores_gemma":[0.8371767,0.000645937,0.1192939,0.001357036,0.000215967,0.000414586,0.03263507,0.001516685,0.006744047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01360348,"threshold_uncertainty_score":0.02739131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07660808466055637,"score_gpt":0.3909621699752346,"score_spread":0.3143540853146783,"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."}}