{"id":"W2003562462","doi":"10.3390/a2020623","title":"Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce","year":2009,"lang":"en","type":"article","venue":"Algorithms","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; University of Pittsburgh","keywords":"Shelf life; Postharvest; Lactuca; Environmental science; Greenhouse; Horticulture; Meteorology; Food science; Chemistry; Physics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004263298,0.0004680906,0.0002520663,0.0002718364,0.0001352711,0.0003349048,0.0002704508,0.0003606291,0.0004054356],"category_scores_gemma":[0.0009108055,0.000177243,0.0002879309,0.0002260933,0.0001164549,0.0002909171,0.0001382223,0.0003101347,0.0000998187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008246228,"about_ca_system_score_gemma":0.0003972707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0190979,"about_ca_topic_score_gemma":0.01487703,"domain_scores_codex":[0.9999249,0.00002128333,0.000006178781,0.00002265158,0.00001567768,0.000009257758],"domain_scores_gemma":[0.9996796,0.0001987865,0.00004071603,0.000009830817,0.00006311468,0.000007906165],"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":[0.00004226776,0.00002077091,0.002460931,0.00001287874,0.00001480846,0.0000101907,0.000009708252,0.9883144,0.001366888,0.0000909628,0.00007417282,0.007581969],"study_design_scores_gemma":[7.590837e-7,0.000008281534,0.0004541887,7.612441e-7,0.000001679139,9.950752e-7,0.000001055811,0.9991635,0.0002906853,0.00005417121,0.00002274085,0.000001182219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.869518,0.0005114426,0.1273872,0.0001030748,0.00002926145,0.00003118914,0.0002301642,0.0003043059,0.001885355],"genre_scores_gemma":[0.9891678,0.0001180138,0.009378279,0.00001211531,0.000005582556,0.00003897641,0.000187284,0.000008553004,0.001083373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0190979,"threshold_uncertainty_score":0.03797346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01929254372916698,"score_gpt":0.2192305979188266,"score_spread":0.1999380541896596,"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."}}