{"id":"W1576139011","doi":"10.1002/env.2304","title":"Statistical modeling and forecasting of fruit crop phenology under climate change","year":2014,"lang":"en","type":"article","venue":"Environmetrics","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge; Agriculture and Agri-Food Canada; University of British Columbia; Carleton University","funders":"Agriculture and Agri-Food Canada; Government of Canada; Australian Government","keywords":"Phenology; Climate change; Bloom; Environmental science; Growing degree-day; Climatology; Global warming; Climate model; Econometrics; Ecology; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003240253,0.0001341651,0.0001995072,0.00006245115,0.00009307809,0.00001357824,0.0001126848,0.0001207676,0.000164741],"category_scores_gemma":[0.0002560874,0.0001107621,0.00002559397,0.0002708531,0.0002250417,0.00009475145,0.0002682281,0.0001599715,0.00008752572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006116605,"about_ca_system_score_gemma":9.388202e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001104249,"about_ca_topic_score_gemma":0.00001764429,"domain_scores_codex":[0.9988108,0.0000731564,0.0002388475,0.0003053579,0.0002601064,0.0003117591],"domain_scores_gemma":[0.9993834,0.000229509,0.0001032801,0.0001910214,0.000003403821,0.00008932952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003898822,0.0002204205,0.1363488,0.00009997631,0.00003246897,0.00001609836,0.0011193,0.2735569,0.01527074,0.007288482,0.0003499082,0.565658],"study_design_scores_gemma":[0.0003555001,0.0001266035,0.2103228,0.00001541308,0.00003186812,0.00003238066,0.00008451267,0.784482,0.0002953692,0.002730867,0.001226941,0.0002957236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8882967,0.00009006399,0.106412,0.0001080132,0.00008808522,0.000122532,0.000009560868,0.0000219495,0.00485107],"genre_scores_gemma":[0.9633895,0.0001278263,0.03620615,0.0001520386,0.00006114982,9.980455e-7,0.000006690704,0.00001653514,0.00003915642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5653622,"threshold_uncertainty_score":0.4516748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04195417324822428,"score_gpt":0.228569430678195,"score_spread":0.1866152574299707,"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."}}