{"id":"W2789547868","doi":"10.1016/j.agwat.2018.03.018","title":"Farm level economic analysis of subsurface drip irrigation in Ontario corn production","year":2018,"lang":"en","type":"article","venue":"Agricultural Water Management","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Net present value; Investment (military); Irrigation; Environmental science; Production (economics); Capital investment; Drip irrigation; Agricultural economics; Economics; Present value; Value (mathematics); Agricultural science; Agricultural engineering; Mathematics; Statistics; Agronomy; Microeconomics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003422812,0.0001696766,0.0002327288,0.00007458529,0.0001243201,0.00007939414,0.0002278627,0.00004863628,0.0008659835],"category_scores_gemma":[0.000001951999,0.00005723941,0.0001167887,0.0004492565,0.00004784725,0.0003292299,0.0001466582,0.00007661561,0.0001575408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002434511,"about_ca_system_score_gemma":0.000002218961,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04013366,"about_ca_topic_score_gemma":0.5044814,"domain_scores_codex":[0.9985909,0.00006084638,0.0004222257,0.0004268495,0.0002110213,0.0002881043],"domain_scores_gemma":[0.9996065,0.00001246788,0.0001634557,0.00009880971,0.00007685983,0.00004191778],"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":[0.0007279097,0.001931427,0.3693388,0.0001719514,0.007632564,0.00003940189,0.01642789,0.01746319,0.4259573,0.02633328,0.007208099,0.1267682],"study_design_scores_gemma":[0.0001363176,0.0001057273,0.9669853,0.00001176939,0.000294146,6.288477e-7,0.0007770369,0.0001464753,0.01607936,0.0004638082,0.01480437,0.0001949881],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855523,0.000003599233,0.000007339716,0.001920993,0.0003366322,0.0004391379,0.000008116044,0.00003166198,0.01170021],"genre_scores_gemma":[0.9884384,0.000005673362,0.0001981713,0.00009229038,0.00007635314,0.0000299422,0.0004368514,8.887089e-7,0.01072139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5976465,"threshold_uncertainty_score":0.9662582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03226795999307772,"score_gpt":0.218117747758035,"score_spread":0.1858497877649573,"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."}}