{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008097073,0.0003241533,0.0003415543,0.0004827949,0.0002028285,0.0003724807,0.0005186672,0.0003886742,0.0002962665],"category_scores_gemma":[0.001802103,0.0002474413,0.0003771409,0.0005688353,0.0003365151,0.0004309291,0.0003101501,0.0004027226,0.00006630812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005885344,"about_ca_system_score_gemma":0.0005053082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01670636,"about_ca_topic_score_gemma":0.01505858,"domain_scores_codex":[0.9998612,0.00004839537,0.000007815132,0.00003572858,0.00002778604,0.00001897646],"domain_scores_gemma":[0.9991813,0.000520891,0.000161976,0.00003684032,0.00006359885,0.00003533148],"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":[0.00001178366,0.000007126405,0.00381875,0.000005982383,0.00001007125,0.00001038026,0.000007120549,0.9909508,0.0005033065,0.0005051766,0.00008117685,0.00408814],"study_design_scores_gemma":[9.863346e-7,0.000003106552,0.001346095,4.698819e-7,0.000001229419,0.000001893059,0.00000139584,0.9981547,0.00007999436,0.0003670687,0.00004161074,0.000001431412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8158786,0.0003369878,0.181791,0.0002968753,0.00002437886,0.00001751715,0.0005588551,0.0003338394,0.0007619564],"genre_scores_gemma":[0.98912,0.0002221395,0.009860143,0.00001280445,0.00002001097,0.00001660085,0.0003223812,0.00001652878,0.0004092311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01670636,"threshold_uncertainty_score":0.03321826,"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."}}