{"id":"W3098301394","doi":"10.1186/s40538-020-00191-7","title":"Pre-harvest field application of enhanced freshness formulation reduces yield loss in orange","year":2020,"lang":"en","type":"article","venue":"Chemical and Biological Technologies in Agriculture","topic":"Postharvest Quality and Shelf Life Management","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Global Affairs Canada; International Development Research Centre; Government of Canada; University of Guelph","keywords":"Hexanal; Orange (colour); PEST analysis; Horticulture; Cultivar; Yield (engineering); Biology; Toxicology; Food science","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":[0.0001292311,0.0001803987,0.0002464489,0.0001467673,0.0000950281,0.0002596234,0.0001923821,0.0001643368,0.001680915],"category_scores_gemma":[0.0001400055,0.00007692044,0.0002288243,0.00007115953,0.00008398146,0.0002193097,0.0001389968,0.0004211666,0.00009748319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002180018,"about_ca_system_score_gemma":0.0001322919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001223202,"about_ca_topic_score_gemma":0.002757983,"domain_scores_codex":[0.9999306,0.000009585889,0.00000498953,0.00001766828,0.00002198777,0.00001516014],"domain_scores_gemma":[0.9998609,0.00002722307,0.00004785691,0.000006832123,0.000024611,0.00003260226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005812537,0.0003802003,0.001052951,0.0001283141,0.00001122446,0.00003056815,0.00002072661,0.00009370364,0.9934772,0.00001289107,0.00005145815,0.004159436],"study_design_scores_gemma":[0.00009609372,0.03075477,0.1278756,0.00005318356,0.00009101111,0.000180326,0.000244303,0.001426218,0.8359556,0.0000442629,0.003256051,0.00002268691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989678,0.0004225506,0.0001776898,0.00002301535,0.000007638236,0.000016799,0.00008663948,0.00001149644,0.0002864308],"genre_scores_gemma":[0.9963648,0.0004597952,0.0007636122,0.0000473351,0.000004184331,0.00001918098,0.0002085664,0.000006619724,0.002125926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001680915,"threshold_uncertainty_score":0.005623221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02930707491659632,"score_gpt":0.2387135432252408,"score_spread":0.2094064683086445,"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."}}