{"id":"W2082263501","doi":"10.1016/j.rse.2009.03.007","title":"A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":702,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"","keywords":"Remote sensing; Vegetation (pathology); Temporal resolution; Environmental science; Disturbance (geology); Land cover; Image resolution; Range (aeronautics); Sensor fusion; Geography; Computer science; Land use; Ecology; 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":[0.0002044757,0.0001945642,0.0002616468,0.00003537556,0.00008820452,0.00001308602,0.0001205048,0.00009730786,0.000004376019],"category_scores_gemma":[0.00003882555,0.0001602954,0.00003373997,0.0000612437,0.000126655,0.00008950187,0.0001481259,0.00008448891,0.000001453307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009919368,"about_ca_system_score_gemma":0.000008012224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001105812,"about_ca_topic_score_gemma":0.000111282,"domain_scores_codex":[0.998642,0.0000323821,0.0002895895,0.0004970927,0.0003342843,0.0002047022],"domain_scores_gemma":[0.999052,0.00005585207,0.0002274644,0.0005633048,0.000004082881,0.00009726732],"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":[0.0002226241,0.00009514786,0.002432253,0.00006595428,0.00001501589,0.000004835756,0.0003922668,0.3282638,0.09463654,0.00001879825,0.001676848,0.5721759],"study_design_scores_gemma":[0.0005785369,0.0001550915,0.1025216,0.0001489106,0.00002666835,0.000004730102,0.000009956461,0.8936201,0.001476237,0.0009613547,0.0003371403,0.0001596075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4343327,0.00005169978,0.5640744,0.0009108614,0.00002984282,0.0003843557,0.00003049554,0.00001410164,0.0001715819],"genre_scores_gemma":[0.7605529,0.00005936407,0.2390954,0.00008195962,0.00002717302,1.710919e-8,0.00007530976,0.00001028558,0.00009756753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5720162,"threshold_uncertainty_score":0.6536658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01916921768172049,"score_gpt":0.211607316090757,"score_spread":0.1924380984090365,"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."}}