{"id":"W2810060997","doi":"10.1016/j.dib.2018.06.057","title":"An open-access dataset of crop production by farm size from agricultural censuses and surveys","year":2018,"lang":"en","type":"article","venue":"Data in Brief","topic":"Agricultural Innovations and Practices","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council; Social Sciences and Humanities Research Council of Canada","keywords":"Agriculture; Census; Harmonization; Sample size determination; Agricultural productivity; Crop; Agricultural economics; Production (economics); Geography; Agricultural science; Statistics; Environmental science; Mathematics; Population; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000688956,0.0001176733,0.0001640286,0.000006053808,0.0001701421,0.0005332323,0.001503988,0.00006061191,0.0004396643],"category_scores_gemma":[0.0002986407,0.00004048104,0.000005569205,0.0005558773,0.0001421678,0.003366886,0.0009479477,0.00008905598,0.000005413979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007510487,"about_ca_system_score_gemma":0.000004422266,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04513478,"about_ca_topic_score_gemma":0.03334874,"domain_scores_codex":[0.9986913,0.0002309324,0.0002849368,0.0005066718,0.0001356032,0.0001505701],"domain_scores_gemma":[0.9991616,0.0002113097,0.0002143021,0.0002339047,0.0001300926,0.00004879014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005409505,0.0003845278,0.08475661,0.000006682271,0.00002145265,0.000001379428,0.00005514607,4.644934e-7,0.6764438,0.00006151538,0.1765076,0.06170674],"study_design_scores_gemma":[0.000089687,0.0001271313,0.8348697,0.0000108971,0.000007910326,0.000003288972,0.0001939783,0.000009843346,0.005906336,0.00004838853,0.1585973,0.0001355543],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9625472,0.00008089007,0.000001380976,0.001156762,0.000119059,0.00021481,0.03572254,0.00001558149,0.0001417989],"genre_scores_gemma":[0.9170606,0.00008584375,0.0002719129,0.0001896497,0.0002860716,0.000006373212,0.08205555,6.727614e-7,0.00004336638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7501131,"threshold_uncertainty_score":0.9842901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1243028839742485,"score_gpt":0.371953376524316,"score_spread":0.2476504925500675,"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."}}