{"id":"W6893648361","doi":"10.5281/zenodo.3372666","title":"Liste des indicateurs potentiels des avantages de la science ouverte pour les Canadiennes et les Canadiens","year":2019,"lang":"fr","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Agriculture and Agri-Food Canada; Canadian Space Agency; Health Canada; Fisheries and Oceans Canada; Environment and Climate Change Canada; Atomic Energy of Canada Limited; Transport Canada; Innovation, Science and Economic Development Canada; Polar Knowledge Canada; Natural Resources Canada; Defence Research and Development Canada; Public Health Agency of Canada","keywords":"Popular science; Public access; Context (archaeology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001890649,0.0002199411,0.0001754934,0.0006826306,0.003913795,0.003052214,0.001087832,0.000101386,0.0112904],"category_scores_gemma":[0.0008259875,0.0002446908,0.00006379472,0.001152157,0.00147432,0.001814166,0.000962212,0.0003494155,0.004415755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005714458,"about_ca_system_score_gemma":0.00003818,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02391413,"about_ca_topic_score_gemma":0.001166585,"domain_scores_codex":[0.9977917,0.0002531469,0.0002593335,0.0004899343,0.0004022976,0.0008035927],"domain_scores_gemma":[0.9985694,0.00004846311,0.0001897345,0.0002862119,0.0007642193,0.0001419739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001151735,0.0004445981,0.01799683,0.001856988,0.0001613963,0.0001552047,0.01071752,0.000596595,0.03135835,0.05845774,0.1478151,0.7303245],"study_design_scores_gemma":[0.0006349019,0.00006452863,0.0775516,0.0002735584,0.00006602381,0.0001250892,0.007354606,0.001423675,0.0003980728,0.0008369584,0.9109004,0.0003705998],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7977768,0.0008663906,0.0006329949,0.001406149,0.0002980232,0.0003478585,0.00009492268,0.0002786508,0.1982982],"genre_scores_gemma":[0.9808183,0.0004898061,0.0002564987,0.0003394064,0.0001950724,6.195736e-8,0.0002808836,0.0006560534,0.0169639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7630852,"threshold_uncertainty_score":0.9979827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0283000127660535,"score_gpt":0.2491364359622502,"score_spread":0.2208364231961967,"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."}}