{"id":"W2312135019","doi":"","title":"Using multi-resolution remote sensing to monitor disturbance and climate change impacts on Northern forests","year":2015,"lang":"en","type":"dissertation","venue":"OpenBU/Boston University Institutional Repository (Boston University)","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Disturbance (geology); Remote sensing; Climate change; Environmental resource management; Environmental science; Change detection; Geography; Forestry; Geology; Oceanography; Geomorphology","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":[0.000503384,0.0001460325,0.00009456464,0.0006566638,0.0002159227,0.0004281772,0.0001780493,0.00009894919,0.0002377237],"category_scores_gemma":[0.0008936912,0.00009353998,0.0001302511,0.0006263431,0.0001129346,0.0003958853,0.0001783327,0.0001173107,0.00005796377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006051926,"about_ca_system_score_gemma":0.0003109809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04595493,"about_ca_topic_score_gemma":0.1383653,"domain_scores_codex":[0.9998205,0.00003323037,0.00001216837,0.00004427504,0.00006995758,0.00001986325],"domain_scores_gemma":[0.9997132,0.00008514754,0.00009228269,0.00003422062,0.00005509079,0.00002012427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009468987,0.0001167187,0.8697988,0.00005955164,0.00008331303,0.00007208959,0.0004645124,0.01667821,0.01569787,0.0002568332,0.0003903653,0.09628695],"study_design_scores_gemma":[0.000004302499,0.00002902142,0.9730945,0.000009045362,0.00002219161,0.00003427408,0.0002921822,0.02291149,0.002588844,0.0001033637,0.0009000939,0.00001069689],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955254,0.0001598333,0.00259064,0.00003317451,0.000003561076,0.00001383299,0.0003361875,0.00004041122,0.00129706],"genre_scores_gemma":[0.9906958,0.0001612457,0.008444617,0.00001732396,0.00000562304,0.00001217979,0.0003850051,0.000004989173,0.0002732043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04595493,"threshold_uncertainty_score":0.09137493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04572026682584304,"score_gpt":0.2583136812692693,"score_spread":0.2125934144434262,"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."}}