{"id":"W2041144461","doi":"10.1111/j.1467-9353.2004.00208.x","title":"Is Geographical Targeting Cost-Effective? The Case of the Conservation Reserve Enhancement Program in Illinois","year":2005,"lang":"en","type":"article","venue":"Review of Agricultural Economics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"University of Guelph; Farm Service Agency; University of New South Wales; Illinois Department of Natural Resources; U.S. Department of Agriculture","keywords":"Conservation Reserve Program; Natural resource economics; Economics; Nature reserve; Business; Geography; Archaeology","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":[0.001046011,0.0001426914,0.0004347919,0.00003614329,0.00007527483,0.00001544854,0.000235978,0.00006778825,0.0002007596],"category_scores_gemma":[0.00007709623,0.00008909129,0.0002540161,0.000183707,0.0001136784,0.0002266727,0.00009145868,0.0001432799,0.00004227677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002184774,"about_ca_system_score_gemma":0.00000943368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004216738,"about_ca_topic_score_gemma":0.0003149259,"domain_scores_codex":[0.9983532,0.00005303794,0.001138308,0.0002624106,0.0000198432,0.0001731832],"domain_scores_gemma":[0.9986705,0.00008369369,0.0009204284,0.0002682522,0.00002717505,0.00002998695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003712441,0.001227264,0.6732343,0.004469169,0.0004537057,0.000002083559,0.001815609,0.004081729,0.0001818373,0.08643613,0.01234174,0.2157193],"study_design_scores_gemma":[0.0009442658,0.0001566371,0.8817804,0.001119703,0.00005847423,0.0000396305,0.0003889066,0.006164541,0.002074904,0.001338822,0.1054968,0.0004368933],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685095,0.02056231,0.0000110871,0.0073577,0.0001000387,0.00249665,0.00006151215,0.000005170792,0.0008960292],"genre_scores_gemma":[0.9500459,0.04757361,0.0005920819,0.001203123,0.00006039414,0.0004243613,0.00003155078,0.00000811112,0.00006090322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2152824,"threshold_uncertainty_score":0.3633037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04913515172352104,"score_gpt":0.2526191783819521,"score_spread":0.203484026658431,"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."}}