{"id":"W2346872483","doi":"10.7603/s40872-015-0002-7","title":"Long-Term Yield Prediction of Greenhouse Sweet Pepper Crops","year":2016,"lang":"en","type":"article","venue":"GSTF Journal on Agricultural Engineering","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université de Moncton; University of Waterloo","funders":"","keywords":"Pepper; Greenhouse; Capsicum annuum; Yield (engineering); Term (time); Mathematics; Horticulture; Agricultural engineering; Engineering; Biology","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":[0.0001509412,0.000203745,0.0002396178,0.00003158436,0.0001299531,0.00003244132,0.0002884861,0.0001848067,0.000363145],"category_scores_gemma":[0.0001086558,0.00005429529,0.0001792933,0.0002078272,0.00003422449,0.0002379264,0.00004173295,0.0002773285,0.00004836101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005006756,"about_ca_system_score_gemma":0.000003037513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000550407,"about_ca_topic_score_gemma":0.00002986732,"domain_scores_codex":[0.9988567,0.00002066539,0.0003505727,0.000188073,0.0002394302,0.0003445948],"domain_scores_gemma":[0.9993821,0.0001620333,0.0001592903,0.00006095217,0.0001062974,0.0001293469],"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":[0.00003509591,0.00005710356,0.02350062,0.000007036196,0.00003896267,0.00002107323,0.00001744723,0.00005098759,0.9476117,0.0002980466,0.0004263171,0.02793557],"study_design_scores_gemma":[0.0003365441,0.0004808958,0.9724973,0.0002813987,0.00002447936,0.0002699044,0.0000307726,0.00001469595,0.02541011,0.00001591517,0.0004679841,0.0001699954],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973207,0.0001401866,0.00007510508,0.001655338,0.000337369,0.00010961,0.00003872693,0.000210093,0.000112862],"genre_scores_gemma":[0.9989778,0.0001984089,0.00002672304,0.00004812034,0.0004129282,0.000006886567,0.000005390518,0.000002172663,0.0003215805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9489967,"threshold_uncertainty_score":0.3976183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361974415254674,"score_gpt":0.1830827194787231,"score_spread":0.1694629753261764,"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."}}