{"id":"W3111681889","doi":"10.1109/smc42975.2020.9283138","title":"Yield forecast of California strawberry: Time-series Models vs. ML Tools","year":2020,"lang":"en","type":"article","venue":"","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Yield (engineering); Series (stratigraphy); Time series; Computer science; Environmental science; Machine learning; Geology; Materials 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008100144,0.0005101842,0.0002979548,0.0003958213,0.0001377191,0.0007271085,0.0005542067,0.0003065735,0.0009098224],"category_scores_gemma":[0.001902734,0.0001527006,0.0003477908,0.0003900701,0.0001093592,0.0006656994,0.0002373851,0.0005526912,0.000254256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000472274,"about_ca_system_score_gemma":0.0005139345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03237834,"about_ca_topic_score_gemma":0.02201469,"domain_scores_codex":[0.9998325,0.00005005838,0.00001017152,0.00006243945,0.00003025719,0.00001458763],"domain_scores_gemma":[0.9995238,0.0002972758,0.00005480344,0.00003234192,0.00007731236,0.00001449726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001747978,0.00007377753,0.01368254,0.00006077403,0.00006926687,0.00005995337,0.00003766305,0.9456814,0.001052729,0.001038819,0.001013087,0.03705517],"study_design_scores_gemma":[0.000003286156,0.00001932404,0.00283177,0.000003600919,0.000008302916,0.000004165993,0.00001560571,0.9963878,0.0003317247,0.000181158,0.000208778,0.000004518361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8787819,0.0009641533,0.1118444,0.0007389052,0.00009763231,0.00004408736,0.001827116,0.0009925187,0.00470926],"genre_scores_gemma":[0.9880775,0.0002396344,0.009461528,0.00003050287,0.00002511815,0.0000208879,0.001007666,0.00002158662,0.001115614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03237834,"threshold_uncertainty_score":0.06437975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04619703827883648,"score_gpt":0.1893009876658153,"score_spread":0.1431039493869788,"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."}}