{"id":"W1975261891","doi":"10.1016/j.jag.2010.12.003","title":"Normalized algorithm for mapping and dating forest disturbances and regrowth for the United States","year":2011,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"U.S. Forest Service","keywords":"Disturbance (geology); Forest ecology; Environmental science; Ecosystem; Remote sensing; Geography; Carbon cycle; Forest inventory; Algorithm; Forestry; Forest management; Ecology; Mathematics; Geology","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":[0.0005983431,0.00007728742,0.00009215215,0.00006622339,0.0001359159,0.00009396932,0.0001001148,0.00002912576,0.00001361008],"category_scores_gemma":[0.00005509913,0.00005466733,0.00002113457,0.0000678787,0.00005458111,0.000710726,0.00003872147,0.00005220868,9.922352e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000172807,"about_ca_system_score_gemma":0.000005270471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001322021,"about_ca_topic_score_gemma":0.00004876847,"domain_scores_codex":[0.9992967,0.000008236817,0.0003538044,0.00006728801,0.0001869256,0.00008705232],"domain_scores_gemma":[0.9991691,0.0002359643,0.0004300807,0.00004014192,0.00008698626,0.00003772025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005608546,0.00004390069,0.06413051,0.0001292581,0.0002034953,8.328792e-7,0.01383152,0.001949392,0.0005179021,0.005586747,0.001111183,0.9119344],"study_design_scores_gemma":[0.00134718,0.0000932703,0.2659006,0.00003881336,0.00001774866,0.00002146013,0.001051634,0.7111509,0.0003616485,0.001665921,0.01825802,0.00009271835],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7882803,0.00003928966,0.2103474,0.0003503656,0.0001907936,0.0004801362,0.00002158216,0.000009574858,0.0002806037],"genre_scores_gemma":[0.9471573,0.0001921979,0.05175486,0.0005968611,0.00009936639,0.00004232359,0.0001106011,0.000007915944,0.00003854689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9118417,"threshold_uncertainty_score":0.2229269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02121642356510674,"score_gpt":0.2161085363536779,"score_spread":0.1948921127885712,"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."}}