{"id":"W2396634530","doi":"","title":"SEBI: An Architecture for Biomedical Image Discovery, Interoperability and Reusability Based on Semantic Enrichment.","year":2014,"lang":"en","type":"article","venue":"SWAT4LS","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Interoperability; Information retrieval; Reusability; Key (lock); Information extraction; World Wide Web; Annotation; Semantic search; Semantic Web; Artificial intelligence; Software","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":[0.0005980366,0.0001678915,0.0001980678,0.00003243747,0.00008401291,0.00004516675,0.000198538,0.0001917699,0.00001096359],"category_scores_gemma":[0.0009059709,0.0001190514,0.00007875786,0.00004734248,0.0005162024,0.000003908514,0.00009062671,0.0001201065,0.000002274761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001163038,"about_ca_system_score_gemma":0.00003558291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000167543,"about_ca_topic_score_gemma":0.00002846627,"domain_scores_codex":[0.998693,0.0001621304,0.0001898043,0.0005583352,0.0001328956,0.0002637953],"domain_scores_gemma":[0.999135,0.0001088299,0.00004153781,0.0005291729,0.00003622733,0.0001492042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001397024,0.001258223,0.009488838,0.0004858211,0.00008268916,0.000003632517,0.0002972508,0.0000194528,0.6603423,0.0002315282,0.005850194,0.320543],"study_design_scores_gemma":[0.007797281,0.02245542,0.05073296,0.0002198772,0.000149695,0.00003825797,0.000472611,0.01121806,0.1812343,0.009625394,0.714327,0.001729138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8752862,0.00005432418,0.1216053,0.002303601,0.0001679372,0.0002570889,0.00005926662,0.00003573663,0.0002305314],"genre_scores_gemma":[0.9864576,0.000005865135,0.01217212,0.0007908836,0.0002221659,0.00003641769,0.0001839722,0.00001443763,0.0001165323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7084768,"threshold_uncertainty_score":0.4854777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008014079356497,"score_gpt":0.2797402662734281,"score_spread":0.2696601254798632,"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."}}