{"id":"W1598794494","doi":"10.1002/dvg.22873","title":"Xenbase: Core features, data acquisition, and data processing","year":2015,"lang":"en","type":"article","venue":"genesis","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Biotechnology and Biological Sciences Research Council; National Institutes of Health; Wellcome Trust","keywords":"Core (optical fiber); Computer science; Data acquisition; Telecommunications; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.005176036,0.002617798,0.002336833,0.006310219,0.00140074,0.006344045,0.006019357,0.001197444,0.04022465],"category_scores_gemma":[0.01679687,0.00150549,0.00112452,0.007540009,0.0007877811,0.00422624,0.005742384,0.002224139,0.0508673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001443132,"about_ca_system_score_gemma":0.003658054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005486862,"about_ca_topic_score_gemma":0.003947583,"domain_scores_codex":[0.9964669,0.0004380383,0.0007968135,0.0007655769,0.00127295,0.0002597767],"domain_scores_gemma":[0.9941719,0.00115679,0.0004893634,0.001497036,0.002061865,0.000623027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001241852,0.0001576746,0.004593137,0.002406652,0.0001511486,0.0005121754,0.0004000091,0.002307043,0.007359781,0.004589542,0.8905821,0.08569869],"study_design_scores_gemma":[0.000306883,0.0001101587,0.005767057,0.0005749586,0.0001298115,0.0006154274,0.0002320189,0.01030413,0.02160437,0.009865609,0.9503086,0.0001809384],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.005035161,0.001422331,0.1108997,0.0009764694,0.0004149017,0.002236638,0.5801455,0.2816772,0.01719214],"genre_scores_gemma":[0.01474639,0.001111827,0.1230574,0.0009443683,0.0001745261,0.004345273,0.8265142,0.02415808,0.004947992],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04022465,"threshold_uncertainty_score":0.1345648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1302921301307467,"score_gpt":0.3598876030192796,"score_spread":0.2295954728885329,"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."}}