{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001429754,0.00135732,0.001097774,0.006981793,0.000542918,0.001468529,0.001502884,0.0008385534,0.01236746],"category_scores_gemma":[0.005206208,0.0005756269,0.0008372128,0.01305162,0.0002070586,0.001141829,0.0009242337,0.001059353,0.01216992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004392088,"about_ca_system_score_gemma":0.007077072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4328914,"about_ca_topic_score_gemma":0.3528811,"domain_scores_codex":[0.9987746,0.0001512738,0.0001797357,0.0001544867,0.0005672035,0.0001725829],"domain_scores_gemma":[0.9966536,0.0002085823,0.0003904193,0.0001556812,0.002358197,0.0002336154],"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":[0.0001135587,0.00006214634,0.02430811,0.0008350427,0.00009958972,0.00007379722,0.0002142692,0.0006196306,0.0002071171,0.001439524,0.9475482,0.02447891],"study_design_scores_gemma":[0.0001022399,0.00005235645,0.3724138,0.000366055,0.00008365309,0.000141486,0.0005775736,0.0006921999,0.0003209177,0.0004640381,0.6247476,0.00003802876],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.007728851,0.001255513,0.0005834035,0.0006135075,0.0005092327,0.0001295701,0.9829804,0.0002790743,0.00592048],"genre_scores_gemma":[0.02812339,0.002915601,0.002261195,0.0005055253,0.0002302786,0.0007874659,0.9451228,0.0001393528,0.01991431],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4328914,"threshold_uncertainty_score":0.8607434,"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."}}