{"id":"W4400401143","doi":"10.7191/jeslib.907","title":"Identifying metadata commonalities across restricted health data sources: A mixed methods study exploring how to improve the discovery of and access to restricted datasets","year":2024,"lang":"en","type":"article","venue":"Journal of eScience Librarianship","topic":"Intellectual Property Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Dementia Research Alliance; University of Toronto; National Research Council Canada; Canadian Respiratory Research Network; University of Saskatchewan","funders":"","keywords":"Computer science; Chemistry","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","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2125939,0.000589511,0.001289499,0.009582081,0.003517796,0.006889492,0.002856404,0.001477699,0.002549458],"category_scores_gemma":[0.346612,0.001005967,0.002879195,0.008397339,0.003387371,0.008675629,0.007792844,0.002052751,0.0002913084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006855306,"about_ca_system_score_gemma":0.01340949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01017256,"about_ca_topic_score_gemma":0.02051155,"domain_scores_codex":[0.8477796,0.1175424,0.01442411,0.007774158,0.01050428,0.001975451],"domain_scores_gemma":[0.4485392,0.4528743,0.04222191,0.02221064,0.03116839,0.002985641],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.001239417,0.001081601,0.4651333,0.01287589,0.001990574,0.0009057164,0.2917588,0.0006668877,0.001678266,0.01537531,0.003110015,0.2041842],"study_design_scores_gemma":[0.0005579081,0.002922878,0.2667502,0.02675943,0.00397552,0.001562324,0.601426,0.008575057,0.006540378,0.02623228,0.0542392,0.0004588254],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.902486,0.007075108,0.0687977,0.004812404,0.0001326991,0.00886451,0.002332767,0.000121342,0.005377561],"genre_scores_gemma":[0.8438188,0.002623107,0.1387488,0.002067459,0.00007136777,0.01011374,0.001736232,0.00009093443,0.0007295887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9931105,"threshold_uncertainty_score":0.9710119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4708980200227392,"score_gpt":0.4873116199781883,"score_spread":0.01641359995544911,"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."}}