{"id":"W7113458907","doi":"","title":"Identifying and evaluating restricted data sources in Canada: Recommendations for improving data discovery and access","year":2022,"lang":"","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scope (computer science); Data discovery; Knowledge extraction; Data collection; Key (lock); Data quality","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.1654234,0.00220575,0.004391696,0.03626198,0.02053858,0.03583267,0.01292202,0.00418052,0.01460701],"category_scores_gemma":[0.4622521,0.002261879,0.003929609,0.0634161,0.0112333,0.0252634,0.02512782,0.00631129,0.006766479],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1124545,"about_ca_system_score_gemma":0.4808321,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.981917,"about_ca_topic_score_gemma":0.9848735,"domain_scores_codex":[0.8701715,0.04160376,0.016604,0.008057089,0.05035804,0.01320571],"domain_scores_gemma":[0.3426126,0.1503551,0.02205059,0.04692442,0.4082048,0.02985247],"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":[0.0007088274,0.0002360869,0.1172892,0.005351857,0.0005801933,0.0006296497,0.01503579,0.004506301,0.002614699,0.07382987,0.4828934,0.2963242],"study_design_scores_gemma":[0.0002764746,0.00007372174,0.06867862,0.01165753,0.0005908585,0.0002362577,0.03666856,0.006799209,0.004754957,0.05430402,0.8152353,0.0007245615],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.05434714,0.02764949,0.1428771,0.4386782,0.003431004,0.01156111,0.15821,0.008028459,0.1552175],"genre_scores_gemma":[0.146255,0.02554428,0.6266105,0.03309803,0.0006897206,0.006813992,0.1299789,0.003171559,0.02783797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.987078,"threshold_uncertainty_score":0.8748531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.135747327194445,"score_gpt":0.3736615614049591,"score_spread":0.2379142342105141,"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."}}