{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.005751842,0.001557321,0.001750618,0.009643725,0.001470302,0.005129296,0.005252631,0.002341413,0.004101867],"category_scores_gemma":[0.009977587,0.001524854,0.002815006,0.007265952,0.00208433,0.009877785,0.01021861,0.003036816,0.0066355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001811552,"about_ca_system_score_gemma":0.002596318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00744838,"about_ca_topic_score_gemma":0.01072441,"domain_scores_codex":[0.9977034,0.0004110107,0.0002972082,0.0005454646,0.0008600768,0.0001829499],"domain_scores_gemma":[0.9957175,0.001023712,0.0004025984,0.001450601,0.0009740332,0.0004315749],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001327018,0.0009634114,0.006465118,0.002856003,0.000983551,0.001705274,0.003557632,0.01274092,0.05181427,0.1181003,0.1291915,0.6702949],"study_design_scores_gemma":[0.0001134457,0.0002509205,0.005087066,0.0006636329,0.0004332127,0.001992214,0.0009581154,0.2620561,0.06010132,0.201806,0.4662085,0.0003295787],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002438434,0.0005744634,0.936996,0.0005994603,0.00007044052,0.0004399702,0.001557191,0.05399605,0.003327884],"genre_scores_gemma":[0.02662702,0.001015197,0.9440066,0.0006694592,0.0000531224,0.0006991923,0.01714549,0.003089473,0.006694433],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9947473,"threshold_uncertainty_score":0.03041905,"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."}}