{"id":"W2105612251","doi":"10.1186/1471-2105-12-s8-s12","title":"A linear classifier based on entity recognition tools and a statistical approach to method extraction in the protein-protein interaction literature","year":2011,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Fundação Luso-Americana para o Desenvolvimento; Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Computer science; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Relationship extraction; Machine learning; Ranking (information retrieval); Natural language processing; Data mining; Information extraction","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":[0.007917657,0.00100389,0.001098665,0.01321548,0.001111656,0.004605927,0.002059927,0.001810503,0.003487555],"category_scores_gemma":[0.02305882,0.0003675112,0.001752469,0.009767845,0.0008793402,0.005134308,0.00187229,0.001767355,0.005246555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001678257,"about_ca_system_score_gemma":0.003477925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004968896,"about_ca_topic_score_gemma":0.007414908,"domain_scores_codex":[0.9924188,0.001895423,0.001292004,0.001747902,0.002280872,0.0003650338],"domain_scores_gemma":[0.977759,0.01343425,0.001504518,0.001506875,0.005296765,0.0004985782],"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":[0.0006613724,0.000389346,0.02637365,0.001214372,0.0002981768,0.0005608738,0.0002860085,0.01151715,0.01600182,0.007192061,0.02489327,0.9106118],"study_design_scores_gemma":[0.0002204977,0.001170243,0.02438964,0.0004227853,0.0006184949,0.001957988,0.0007481516,0.8553806,0.0442949,0.02463037,0.04595951,0.0002067843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08532628,0.006431932,0.8770525,0.00242873,0.0005475073,0.0008513152,0.005953559,0.01530377,0.006104458],"genre_scores_gemma":[0.3436119,0.001952843,0.6311303,0.0006802115,0.0004916651,0.000929134,0.01593804,0.0003521458,0.004913682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01321548,"threshold_uncertainty_score":0.0418731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09354681556915123,"score_gpt":0.3289282897809513,"score_spread":0.2353814742118001,"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."}}