{"id":"W2094137531","doi":"10.1117/1.jrs.7.073541","title":"Monitoring soil disturbance on salvaged areas within the mountain pine beetle infestation using digital imagery","year":2013,"lang":"en","type":"article","venue":"Journal of Applied Remote Sensing","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Forests","funders":"","keywords":"Environmental science; Disturbance (geology); Mountain pine beetle; Landslide; Remote sensing; Logging; Satellite imagery; Forest management; Silviculture; Geology; Forestry; Agroforestry; Geography; Geomorphology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001912314,0.0001527061,0.0001636866,0.0001296916,0.0001131799,0.0002479506,0.00008969496,0.00004421733,0.000002920225],"category_scores_gemma":[0.0000365171,0.0001132076,0.00005830971,0.0002024271,0.00004007817,0.0002622117,0.00002495588,0.0002392271,0.00001510402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000170802,"about_ca_system_score_gemma":0.00001587231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001451669,"about_ca_topic_score_gemma":0.000002848323,"domain_scores_codex":[0.9990268,0.00001316383,0.0004018368,0.00009254848,0.0002932959,0.000172293],"domain_scores_gemma":[0.9994207,0.00005195701,0.0002195242,0.000151114,0.00009372199,0.00006298605],"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":[0.00006044491,0.00002524376,0.000147482,0.00009908285,0.000150089,0.00004482771,0.001147572,0.669907,0.1078391,0.0002531148,0.0006626805,0.2196633],"study_design_scores_gemma":[0.0009320409,0.00005451444,0.007503315,0.0005335634,0.00006785568,0.0001058399,0.002263149,0.9314684,0.05312324,0.002809893,0.0007142631,0.0004239127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.921773,0.00004952162,0.07298407,0.0000985881,0.0006390889,0.0001709573,6.540463e-7,0.00006624335,0.004217941],"genre_scores_gemma":[0.9923958,0.00001882247,0.007127993,0.00006308604,0.0003224232,7.682175e-8,0.000001837889,0.00003621042,0.00003376043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2615614,"threshold_uncertainty_score":0.4616472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01017488031378228,"score_gpt":0.211900089083558,"score_spread":0.2017252087697757,"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."}}