{"id":"W1514701801","doi":"10.1007/978-3-540-68123-6_47","title":"A Dynamic Window Based Passage Extraction Algorithm for Genomics Information Retrieval","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Genomics; Paragraph; Algorithm; Window (computing); Data mining; Focus (optics); Artificial intelligence; Information retrieval; Genome; World Wide Web; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003024691,0.0002332911,0.0002074034,0.0001977744,0.0001427987,0.00006364009,0.000397891,0.0005285153,0.000003869694],"category_scores_gemma":[0.0001354194,0.0002151644,0.0001030745,0.00009034234,0.0004639138,0.00001287513,0.0001162168,0.0002643255,0.000004796284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001172557,"about_ca_system_score_gemma":0.0004247207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003109105,"about_ca_topic_score_gemma":0.00001031268,"domain_scores_codex":[0.9987246,0.00001063444,0.0002787941,0.0004400445,0.0002627723,0.0002831037],"domain_scores_gemma":[0.9991817,0.00008633887,0.0001841055,0.0003424528,0.0001371964,0.00006817388],"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":[0.00002838447,0.000009781937,0.000004218857,0.00001886708,0.000008184568,0.00000403365,0.00004943885,0.001856376,0.002963625,0.000004422761,0.00007084394,0.9949818],"study_design_scores_gemma":[0.001194966,0.0009363169,0.0002234474,0.0001442276,0.00002315393,0.0001155638,8.458731e-7,0.7971282,0.02774755,0.001886698,0.1697885,0.0008105672],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005511504,0.0003661837,0.9976038,0.0001841172,0.0007232401,0.0002854103,0.00004889492,0.00002725294,0.00020998],"genre_scores_gemma":[0.03411909,0.0003775882,0.9624912,0.001619732,0.0005345871,0.0000143602,0.0005107094,0.00003375414,0.0002989737],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9941713,"threshold_uncertainty_score":0.8774148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132105187757909,"score_gpt":0.2531834802643988,"score_spread":0.2418624283868197,"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."}}