{"id":"W7112241261","doi":"","title":"Available upon request (actually): Improving data discovery and access to Canadian restricted data","year":2025,"lang":"","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workflow; Data discovery; Data access; Data collection; Data curation; Knowledge extraction; 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":[0.07135993,0.001091808,0.001852648,0.01604772,0.02327882,0.02581076,0.006103162,0.002811892,0.0722765],"category_scores_gemma":[0.240635,0.001813806,0.002002284,0.03008901,0.008651262,0.0114403,0.02103757,0.0040094,0.02501231],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08686925,"about_ca_system_score_gemma":0.4619143,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9768072,"about_ca_topic_score_gemma":0.976714,"domain_scores_codex":[0.9319119,0.01334098,0.004767317,0.005202292,0.03556272,0.009214781],"domain_scores_gemma":[0.6530871,0.06255241,0.009524968,0.05151581,0.1906129,0.03270683],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004791337,0.0001050303,0.01735508,0.001678567,0.0001344179,0.0005956075,0.01794313,0.0005748437,0.001885205,0.04305366,0.7716786,0.1445168],"study_design_scores_gemma":[0.00005801166,0.00002433133,0.01415325,0.0008783625,0.00004969578,0.00007671149,0.007366787,0.0007064511,0.001230462,0.007162453,0.9681018,0.000191722],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02882554,0.004799122,0.05157754,0.135261,0.00257429,0.00611275,0.2913592,0.01752727,0.4619633],"genre_scores_gemma":[0.1746603,0.01069578,0.2677536,0.03134035,0.0008062808,0.006528801,0.2284352,0.01338286,0.2663969],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9938968,"threshold_uncertainty_score":0.6302835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04896843978826176,"score_gpt":0.3035773596195319,"score_spread":0.2546089198312701,"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."}}