{"id":"W2339952807","doi":"10.7717/peerj.2331","title":"The health care and life sciences community profile for dataset descriptions","year":2016,"lang":"en","type":"article","venue":"PeerJ","topic":"Research Data Management Practices","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Toronto","funders":"National Bioscience Database Center; National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute; Biotechnology and Biological Sciences Research Council; National Institutes of Health","keywords":"Metadata; Computer science; Information retrieval; Data element; Search engine indexing; Automatic summarization; RDF; Identification (biology); World Wide Web; Annotation; Metadata modeling; Semantic Web; Artificial intelligence","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"],"consensus_categories":[],"category_scores_codex":[0.04345211,0.001000789,0.001347308,0.008233606,0.001946108,0.006482453,0.004630112,0.006446792,0.01376873],"category_scores_gemma":[0.07120333,0.001101388,0.002129831,0.01108659,0.001587708,0.008005304,0.007410218,0.005588722,0.02101831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005427602,"about_ca_system_score_gemma":0.01913754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02157754,"about_ca_topic_score_gemma":0.03496584,"domain_scores_codex":[0.9761391,0.0082597,0.009008875,0.000927637,0.004765581,0.0008991674],"domain_scores_gemma":[0.9355639,0.02643489,0.003351315,0.01211665,0.02010883,0.002424448],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001501405,0.0001696246,0.001763468,0.002838707,0.00004408776,0.0004771223,0.001779081,0.001306916,0.004512329,0.1481661,0.719561,0.1192314],"study_design_scores_gemma":[0.0000279312,0.00002319149,0.0005752296,0.001022292,0.00001805261,0.0002233187,0.0001833188,0.0006543857,0.0006756186,0.0108215,0.9857396,0.00003554166],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003453227,0.003611589,0.5728724,0.0889852,0.002435449,0.01259609,0.1428727,0.0139782,0.1591952],"genre_scores_gemma":[0.01025517,0.005410239,0.6659997,0.04526224,0.000848031,0.008412281,0.2228379,0.003154437,0.03782005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9565479,"threshold_uncertainty_score":0.2297995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2570925896335336,"score_gpt":0.440553660590544,"score_spread":0.1834610709570104,"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."}}