{"id":"W6912607064","doi":"10.5281/zenodo.400192","title":"Contextual Analysis Of The Reference Countries: Canada","year":2015,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Research, Science, and Academia","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Boosting (machine learning); Raw data; Relation (database); Context analysis; Principal (computer security); Order (exchange); Work (physics)","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":[],"consensus_categories":[],"category_scores_codex":[0.001363334,0.0004088978,0.0006267994,0.005258195,0.01287464,0.006835071,0.001150613,0.0006927054,0.007006339],"category_scores_gemma":[0.006035965,0.0002688443,0.0003493878,0.0225326,0.004250741,0.001543502,0.003379545,0.001075987,0.0003174183],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08641762,"about_ca_system_score_gemma":0.1034828,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9905806,"about_ca_topic_score_gemma":0.9932415,"domain_scores_codex":[0.9974437,0.0005339019,0.0001163088,0.0003147499,0.0006280022,0.0009633809],"domain_scores_gemma":[0.9956009,0.001263394,0.0002448586,0.0001827575,0.002307013,0.0004010031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003690225,0.0001388799,0.1374727,0.002463332,0.0001298781,0.01021316,0.4715176,0.003794156,0.002174255,0.234248,0.05353492,0.08394399],"study_design_scores_gemma":[0.00001147749,0.0000214623,0.07743438,0.001192949,0.00007119272,0.0004747153,0.5785876,0.0005783136,0.0009959397,0.002811293,0.3377309,0.00008973431],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.681775,0.005312816,0.003513806,0.007203224,0.0001708997,0.0009862089,0.02499719,0.00006809535,0.2759729],"genre_scores_gemma":[0.9770212,0.003282505,0.003210131,0.0009532826,0.00001268508,0.0003710492,0.004172284,0.00005650677,0.01092029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9135824,"threshold_uncertainty_score":0.6270066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1723319700108082,"score_gpt":0.3459585552425374,"score_spread":0.1736265852317292,"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."}}