{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00274695,0.00004965247,0.00005588807,0.00004287269,0.002039553,0.001173833,0.001998125,0.000008784836,0.000001569359],"category_scores_gemma":[0.002109324,0.00002561702,0.00001078963,0.000158648,0.0002029232,0.007149891,0.001101002,0.00007736164,0.00001228207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002568346,"about_ca_system_score_gemma":0.0001664011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003285398,"about_ca_topic_score_gemma":0.0004367534,"domain_scores_codex":[0.9988937,0.000315369,0.00009799105,0.0001842892,0.0002458222,0.0002627877],"domain_scores_gemma":[0.9974856,0.00149396,0.0000680032,0.0007925344,0.00006058167,0.00009926836],"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":[0.000005217209,0.00003183909,0.0006498003,0.00007052502,0.00001317378,3.306278e-7,0.0007247968,4.004343e-7,0.00005714459,0.2914458,0.6163226,0.09067839],"study_design_scores_gemma":[0.0001852274,0.0002927465,0.003253042,0.0000153528,0.000001981328,0.000001138135,0.001284572,0.0004507696,0.00004537412,0.001179391,0.9932451,0.00004528866],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008819954,0.004409121,0.771203,0.2046007,0.0004532304,0.001872322,0.005168535,0.0001896178,0.00328352],"genre_scores_gemma":[0.7085109,0.009932575,0.2662472,0.007999181,0.0002835027,0.0009776832,0.001179771,0.00002961396,0.004839592],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6996909,"threshold_uncertainty_score":0.999863,"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."}}