{"id":"W7045286354","doi":"","title":"Agricultural census 2010 : One million people in permanent employment","year":2012,"lang":"en","type":"article","venue":"Gallica (Bibliothèque nationale de France)","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Census; Agriculture; Quarter (Canadian coin); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005520352,0.0001793712,0.000229616,0.0006984103,0.0001124066,0.00005702737,0.000141118,0.0001523211,0.0005957662],"category_scores_gemma":[0.0002987469,0.0001687632,0.00008709644,0.001446019,0.00002024255,0.0005231926,0.00003453505,0.000223102,0.00007859203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003359963,"about_ca_system_score_gemma":0.00005430273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001282738,"about_ca_topic_score_gemma":0.0004110467,"domain_scores_codex":[0.9982663,0.0000883448,0.0004499022,0.0002039862,0.0005657111,0.0004257394],"domain_scores_gemma":[0.9989566,0.0003195786,0.0001752043,0.0001928629,0.0002100458,0.0001457491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003914979,0.00161154,0.7694985,0.0002106813,0.00005004234,0.000001918021,0.003654555,0.0009168483,0.002403437,0.1741129,0.04620831,0.001292181],"study_design_scores_gemma":[0.000608538,0.00001688045,0.9765441,0.00005816676,0.00001656026,0.00001687224,0.00003325309,0.0007827057,0.0003320418,0.006436612,0.01492562,0.0002286914],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910706,0.0008440471,0.001174015,0.002507399,0.0003691686,0.0004885262,0.00004870109,0.0001028966,0.003394625],"genre_scores_gemma":[0.9872676,0.0001804089,0.008769418,0.0001977175,0.0004575009,0.000106361,0.0002341815,0.00002542975,0.00276141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2070456,"threshold_uncertainty_score":0.6881964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04355262225290899,"score_gpt":0.3032114637723821,"score_spread":0.2596588415194731,"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."}}