{"id":"W2046907634","doi":"10.1016/j.rse.2006.06.007","title":"Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Resources Canada; Government of Canada","keywords":"Mountain pine beetle; Dendroctonus; Basal area; Environmental science; Forest inventory; Terrain; Bark beetle; Range (aeronautics); Remote sensing; Ecology; Forestry; Forest management; Geography; Cartography; Agroforestry; 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":[0.0004270888,0.0003769035,0.000278397,0.001112833,0.0002164706,0.0005271429,0.0001921423,0.0003164001,0.0006103436],"category_scores_gemma":[0.001025566,0.0001583972,0.000227485,0.0007633734,0.00005846055,0.0005248536,0.0002024678,0.0001694911,0.000139952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000186934,"about_ca_system_score_gemma":0.0001988497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006540975,"about_ca_topic_score_gemma":0.02277943,"domain_scores_codex":[0.9998172,0.00003848023,0.00001528994,0.00004653484,0.00005332194,0.00002923914],"domain_scores_gemma":[0.9993659,0.0002032337,0.0001099297,0.00007063535,0.0002076407,0.00004272089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007030533,0.0007387647,0.7378691,0.0001123037,0.0003006778,0.0003395711,0.0002721174,0.01982505,0.0861998,0.0001498202,0.0009930335,0.1524966],"study_design_scores_gemma":[0.00003430929,0.0002107443,0.8154867,0.00001167188,0.0001850017,0.0002110751,0.0002243259,0.1726363,0.01005659,0.0001606665,0.000756099,0.00002643208],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953244,0.00009108269,0.003224983,0.00002319389,0.000007268829,0.00001756317,0.000530648,0.0001075435,0.0006732652],"genre_scores_gemma":[0.9919734,0.0000424215,0.006506252,0.0000141541,0.00001002621,0.00001196353,0.001126804,0.000008953141,0.0003060961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006540975,"threshold_uncertainty_score":0.01300579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356586587725085,"score_gpt":0.2139909589787885,"score_spread":0.2004250931015376,"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."}}