{"id":"W2032869846","doi":"10.2481/dsj.6.od26","title":"Canadian National Consultation on Access to Scientific Research Data","year":2007,"lang":"en","type":"article","venue":"Data Science Journal","topic":"Research Data Management Practices","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Université de Montréal","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Strong","keywords":"Scope (computer science); Computer science; Transparency (behavior); Usability; Metadata; Data science; Open data; Reuse; Implementation; World Wide Web; Engineering; Computer security; Software engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["metaresearch","scholarly_communication","open_science"],"category_scores_codex":[0.1153449,0.0001042604,0.00008555254,0.004424845,0.003721329,0.05571972,0.0610731,0.00002882001,0.000042437],"category_scores_gemma":[0.0286426,0.00009212034,0.000008597463,0.009378552,0.0006809213,0.1791516,0.01571452,0.0007426076,0.0005058338],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009058783,"about_ca_system_score_gemma":0.008137026,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006504286,"about_ca_topic_score_gemma":0.06561691,"domain_scores_codex":[0.9893884,0.0001973602,0.0003665139,0.001566692,0.007161078,0.001319925],"domain_scores_gemma":[0.9892991,0.001009704,0.0001581076,0.005559531,0.002416912,0.001556627],"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":[0.00003228793,0.000146129,0.001204467,0.000008186363,0.00001602259,0.0003074604,0.0003529262,0.0002003047,0.001937291,0.1968256,0.683045,0.1159243],"study_design_scores_gemma":[0.0002300197,0.00008314685,0.0227707,0.00004181271,0.000002021133,0.0001048072,0.0002524629,0.03378573,0.0004238111,0.0018177,0.9402866,0.0002011373],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02979269,0.00008459127,0.6988483,0.1551097,0.007028805,0.001855234,0.002705235,0.0001342414,0.1044412],"genre_scores_gemma":[0.8362272,0.0002277161,0.1535375,0.004962541,0.00110115,0.000009323553,0.001544237,0.00002517569,0.002365097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8064345,"threshold_uncertainty_score":0.9975757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6443846780180462,"score_gpt":0.5804227013872811,"score_spread":0.06396197663076508,"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."}}