{"id":"W2908245524","doi":"10.1007/978-3-030-00105-6_8","title":"Predicting the Interior Conditions in a High Tunnel Greenhouse","year":2018,"lang":"en","type":"book-chapter","venue":"Springer proceedings in energy","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Microclimate; Greenhouse; Environmental science; San Joaquin; Glazing; Wind tunnel; Meteorology; Wind speed; Atmospheric sciences; Geotechnical engineering; Engineering; Soil science; Agronomy; Civil engineering; Geography; Geology; Aerospace engineering","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":[0.0001612259,0.0004356391,0.0003569722,0.0001753519,0.0003910871,0.0009275074,0.0004285151,0.0008675667,0.002349293],"category_scores_gemma":[0.0003415934,0.0002705031,0.0005030739,0.0002674744,0.0003235469,0.0006445773,0.0004429146,0.0008123403,0.0002173972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003811905,"about_ca_system_score_gemma":0.0005221233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01630526,"about_ca_topic_score_gemma":0.01817804,"domain_scores_codex":[0.9999684,0.00000580208,8.681099e-7,0.000009757086,0.000005663979,0.000009446364],"domain_scores_gemma":[0.9999225,0.00003939597,0.000005291879,0.000006106852,0.0000104672,0.00001625837],"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":[0.0001103711,0.00008619638,0.01333991,0.00004231661,0.00002165862,0.000188908,0.0000571993,0.9563153,0.01301946,0.001828874,0.001451597,0.01353812],"study_design_scores_gemma":[0.00001568102,0.00004635409,0.009270407,0.000008950979,0.00001115532,0.00003265198,0.00007953725,0.9862493,0.001470816,0.002162873,0.0006334702,0.00001883625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9514838,0.0003870958,0.03422213,0.0003218674,0.0001232985,0.00002634282,0.0006926156,0.0004625225,0.01228042],"genre_scores_gemma":[0.9905155,0.0001758452,0.006903308,0.0000174895,0.00002391227,0.000008678847,0.0003188087,0.00005660345,0.001979766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01630526,"threshold_uncertainty_score":0.03242069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01267142500486186,"score_gpt":0.1965122552605157,"score_spread":0.1838408302556538,"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."}}