{"id":"W3022130220","doi":"","title":"Towards Incorporating Within-Field Variation into Spatial Agronomic Decision Processes","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Agricultural Economics and Policy","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Variation (astronomy); Field (mathematics); Domain (mathematical analysis); Section (typography); Data science; Computer science; Diversity (politics); Variance (accounting); Discipline; Production (economics); Knowledge management; Political science; Business; Sociology; Social science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002245546,0.0003404713,0.0003847878,0.00003914916,0.0003796118,0.0006021134,0.001070439,0.0004125191,0.0002158401],"category_scores_gemma":[0.001501837,0.0001709808,0.0001708948,0.0002779116,0.00006222739,0.0002090611,0.001218404,0.000495786,0.0001025142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001466304,"about_ca_system_score_gemma":0.0002232393,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0233426,"about_ca_topic_score_gemma":0.03174043,"domain_scores_codex":[0.9971231,0.0009664582,0.0006327637,0.0007578611,0.0002446187,0.0002751628],"domain_scores_gemma":[0.9958256,0.001567718,0.000887827,0.0004080852,0.001157014,0.0001536787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009052712,0.0006265636,0.009516635,0.0003949886,0.0001430983,0.00000300439,0.01397602,0.001619146,0.04318986,0.04457799,0.001840657,0.8840215],"study_design_scores_gemma":[0.00206355,0.000028005,0.4034799,0.008742658,0.0002949403,0.00004145969,0.001400558,0.1040464,0.2363815,0.2036832,0.03428957,0.005548378],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9615915,0.0002720218,0.01375417,0.0109651,0.0004421129,0.0005473437,0.0000638347,0.0001479118,0.01221605],"genre_scores_gemma":[0.9860601,0.0002071262,0.01149657,0.0002212185,0.0001872961,0.00004707567,0.0007737763,0.000005214427,0.001001559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8784732,"threshold_uncertainty_score":0.9859278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01249076152334078,"score_gpt":0.2138250490533905,"score_spread":0.2013342875300498,"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."}}