{"id":"W7100354937","doi":"","title":"Data and Text Mining METIS: multiple extraction techniques for informative sentences","year":2008,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Annotation; Support vector machine; Sentence; Component (thermodynamics); Information extraction; Text mining; Metis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005724998,0.001616409,0.001197048,0.01031194,0.0013672,0.002256404,0.001811768,0.00112974,0.007401329],"category_scores_gemma":[0.01878772,0.0007816044,0.001560485,0.006567107,0.0007331696,0.002822754,0.002458669,0.002038976,0.005709688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007798605,"about_ca_system_score_gemma":0.002263532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001204018,"about_ca_topic_score_gemma":0.002716136,"domain_scores_codex":[0.9955089,0.001091206,0.001042573,0.0009491789,0.001258844,0.0001492953],"domain_scores_gemma":[0.9846969,0.00802515,0.00162307,0.001777718,0.003548355,0.0003288143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006628857,0.0003156347,0.006520664,0.003651277,0.0003744907,0.001279684,0.001968012,0.002586572,0.05322624,0.01011249,0.08004003,0.8392621],"study_design_scores_gemma":[0.0005211541,0.000974093,0.02740489,0.001290672,0.001366009,0.006311987,0.003339105,0.3510339,0.2366614,0.06073073,0.3098589,0.0005072462],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02440525,0.001257699,0.8927314,0.001477728,0.0003022452,0.001887708,0.04371061,0.03100782,0.003219419],"genre_scores_gemma":[0.04813468,0.0003395542,0.9064739,0.000158534,0.0001591472,0.001150515,0.04101146,0.0008870419,0.001685199],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01031194,"threshold_uncertainty_score":0.03027701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06496683998000835,"score_gpt":0.3358570197140637,"score_spread":0.2708901797340553,"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."}}