{"id":"W2886976680","doi":"10.1016/j.quascirev.2018.07.009","title":"When tree rings go global: Challenges and opportunities for retro- and prospective insight","year":2018,"lang":"en","type":"article","venue":"Quaternary Science Reviews","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":213,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mount Royal University; Natural Resources Canada","funders":"Horizon 2020 Framework Programme; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Natural Environment Research Council; Sight Research UK; U.S. Department of Agriculture","keywords":"Dendrochronology; Tree (set theory); Climate change; Global change; Sampling (signal processing); Spatial ecology; Scale (ratio); Computer science; Inference; Environmental resource management; Geography; Ecology; Environmental science; Artificial intelligence; Cartography; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.007720751,0.0004583415,0.001079092,0.0008285945,0.001192447,0.006082914,0.0014083,0.003098792,0.007235475],"category_scores_gemma":[0.01526567,0.000331219,0.0005529386,0.001394309,0.006946992,0.0169324,0.003030777,0.004877081,0.0008045811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001566699,"about_ca_system_score_gemma":0.003048205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003496837,"about_ca_topic_score_gemma":0.008658839,"domain_scores_codex":[0.9984021,0.000614522,0.00009646086,0.0002576124,0.0003376004,0.0002917233],"domain_scores_gemma":[0.9906043,0.005117445,0.000923518,0.000935795,0.00165744,0.0007614804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004137145,0.000115768,0.0107582,0.004042158,0.0003043126,0.0004066604,0.009813328,0.003153423,0.004761041,0.3915474,0.1040225,0.4706615],"study_design_scores_gemma":[0.00002522201,0.0001128273,0.0144301,0.002527595,0.0001152996,0.0002584188,0.01582677,0.000688144,0.0007324194,0.4180068,0.5472002,0.00007617594],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.02801562,0.6263658,0.01077544,0.2964929,0.009445136,0.00003486227,0.0005552832,0.0001649236,0.02815001],"genre_scores_gemma":[0.3207175,0.6216461,0.007264677,0.03550358,0.008715542,0.00006914048,0.0003585687,0.0002056608,0.005519222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007720751,"threshold_uncertainty_score":0.04083174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1209853719333719,"score_gpt":0.3031667904740822,"score_spread":0.1821814185407103,"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."}}