{"id":"W6917814903","doi":"10.58079/av2n","title":"Conférence \"The origins of national statistical systems: A cross national history of Argentina, Canada, France and Mexico\"","year":2018,"lang":"en","type":"article","venue":"OpenEdition (OpenEdition)","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"National history; Statistical analysis; Field (mathematics); Population","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":[0.005954382,0.0002196365,0.000454971,0.003121272,0.004713967,0.003748934,0.0008479317,0.0009328133,0.01598362],"category_scores_gemma":[0.012329,0.0003161299,0.0003126558,0.00820244,0.003476621,0.001858251,0.00166695,0.001825386,0.0005889977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03844568,"about_ca_system_score_gemma":0.03492029,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8792793,"about_ca_topic_score_gemma":0.8929448,"domain_scores_codex":[0.9977579,0.0007973688,0.00007703856,0.0002480166,0.0005721622,0.0005476014],"domain_scores_gemma":[0.9929593,0.002169818,0.0006186883,0.0004940396,0.003057124,0.0007010491],"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":[0.0001600485,0.00004398568,0.04767364,0.0003130937,0.00008211826,0.000223218,0.01425005,0.000624083,0.0002023865,0.3309927,0.41725,0.1881846],"study_design_scores_gemma":[0.00001039373,0.0000143345,0.1487346,0.0004482215,0.00002633799,0.00009261578,0.004758406,0.0003143492,0.0001530567,0.00489604,0.8405105,0.00004119968],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1310862,0.1312062,0.01290476,0.3021586,0.004862014,0.0002042659,0.01945182,0.0008121386,0.397314],"genre_scores_gemma":[0.688384,0.07623693,0.01161106,0.006839081,0.002506072,0.000426173,0.00724878,0.0005071429,0.2062407],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1207207,"threshold_uncertainty_score":0.2789443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04369989036169053,"score_gpt":0.3099172745368013,"score_spread":0.2662173841751108,"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."}}