{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004993215,0.0001011303,0.000244094,0.0003907811,0.001373546,0.000753627,0.004125368,0.00004628504,0.007328299],"category_scores_gemma":[0.01120247,0.00006563259,0.00007491271,0.004452697,0.0005691595,0.0003193051,0.001507657,0.0002971457,0.001209402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002287366,"about_ca_system_score_gemma":0.0001167419,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01740764,"about_ca_topic_score_gemma":0.003135074,"domain_scores_codex":[0.9947981,0.0007580773,0.000484594,0.0004420955,0.003152098,0.0003649969],"domain_scores_gemma":[0.9957635,0.000254212,0.0002456071,0.000872285,0.002547298,0.0003170291],"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":[0.00006809904,0.0000397769,0.0007032462,0.000005808846,0.0001139079,0.000005931819,0.002102973,0.0009228694,0.0005400889,0.01404542,0.9566731,0.02477875],"study_design_scores_gemma":[0.0001982073,0.00004924366,0.008955994,0.00000581569,0.00002564761,0.00000689703,0.002457327,0.001860162,0.0002324937,0.0005127804,0.9856098,0.00008564054],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.506842,0.0002115373,0.002520168,0.006919303,0.000409031,0.0006501583,0.001288764,0.0002069807,0.4809521],"genre_scores_gemma":[0.9953414,0.00001800424,0.00002286875,0.0002923927,0.00003714425,1.623568e-8,0.00006844934,0.0001178114,0.004101946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4884994,"threshold_uncertainty_score":0.9999265,"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."}}