{"id":"W2778587136","doi":"10.3390/agriculture7120104","title":"Evaluation of Crop to Crop Water Demand Forecasting: Tomatoes and Bell Peppers Grown in a Commercial Greenhouse","year":2017,"lang":"en","type":"article","venue":"Agriculture","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Greenhouse; Mean squared error; Agricultural engineering; Artificial neural network; Crop; Feedforward neural network; Environmental science; Mathematics; Statistics; Computer science; Agronomy; Engineering; Machine learning; Forestry; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006269125,0.0001484536,0.0002288191,0.00001703137,0.0003893345,0.00006453248,0.0003183696,0.0002137069,0.00009255714],"category_scores_gemma":[0.0001674375,0.00004541787,0.00005199417,0.00007944329,0.0001014956,0.0001549887,0.0001664258,0.0001320443,0.00001340626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001968077,"about_ca_system_score_gemma":0.000004413332,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000615543,"about_ca_topic_score_gemma":0.02218119,"domain_scores_codex":[0.9989178,0.00008023717,0.0002037346,0.0002626466,0.0002623593,0.0002732204],"domain_scores_gemma":[0.9994894,0.00003619561,0.0001016427,0.0001019136,0.000204224,0.00006667314],"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":[0.0001149993,0.0002241965,0.2280257,0.00002799305,0.00005802797,0.00001795249,0.001547025,0.0000447161,0.6291498,0.0002765066,0.0022069,0.1383062],"study_design_scores_gemma":[0.0006178484,0.0002119276,0.9812196,0.00005322876,0.00006522317,0.00001739667,0.0003400547,0.0001628,0.01535877,0.0006785768,0.001083367,0.0001912149],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937516,0.000141642,5.270659e-7,0.004987971,0.00005615137,0.0004093783,0.00001848719,0.00004515278,0.0005890661],"genre_scores_gemma":[0.999648,0.00002053161,0.00003031472,0.000121317,0.00006318545,0.00003765289,0.00001545455,0.000001125453,0.00006239097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7531939,"threshold_uncertainty_score":0.9956614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04272107107856175,"score_gpt":0.2525514899353018,"score_spread":0.2098304188567401,"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."}}