{"id":"W4394397877","doi":"10.6084/m9.figshare.12248312","title":"Dataset for: The importance of incorporating landscape change for predictions of climate-induced plant phenological shifts","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Climate change; Phenology; Geography; Environmental science; Ecology; Physical geography; Environmental resource management; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001237952,0.001089305,0.0008759567,0.001318298,0.0007756593,0.001923684,0.002448065,0.002361943,0.04187635],"category_scores_gemma":[0.00723697,0.000401962,0.001174337,0.002197573,0.0003378871,0.00129824,0.001680035,0.001660766,0.03215747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316148,"about_ca_system_score_gemma":0.001448403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06897297,"about_ca_topic_score_gemma":0.1371186,"domain_scores_codex":[0.9994645,0.00009968864,0.0000582411,0.00018375,0.0001252261,0.00006869151],"domain_scores_gemma":[0.9978651,0.000729166,0.0001892021,0.0005562009,0.0004766739,0.0001836408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002343927,0.0000637254,0.0112535,0.000844497,0.0002185714,0.00009521099,0.00007146892,0.004147547,0.0005759376,0.001493956,0.9710462,0.009955124],"study_design_scores_gemma":[0.0008833186,0.00005399523,0.0415186,0.0004452018,0.00008265514,0.0001314446,0.0002295359,0.0171374,0.0009513489,0.005887028,0.9325636,0.0001158431],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001062717,0.00006563117,0.0003918009,0.0002471451,0.00005919429,0.00002500222,0.9953726,0.001446359,0.001329533],"genre_scores_gemma":[0.004764137,0.00004163144,0.002285991,0.00009994362,0.00001723974,0.0001125125,0.9916993,0.0002536581,0.0007255177],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06897297,"threshold_uncertainty_score":0.1400903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08994997164243465,"score_gpt":0.2795042196105777,"score_spread":0.189554247968143,"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."}}