{"id":"W2105308990","doi":"10.1109/igarss.2007.4424057","title":"Assessing North American forest disturbance from the landsat archive","year":2007,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Disturbance (geology); Environmental science; Ecosystem; Satellite imagery; Biomass (ecology); Physical geography; Satellite; Remote sensing; Geography; Geology; Oceanography; Ecology; 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":[0.0004871348,0.0002566555,0.0001939151,0.003079794,0.0004150722,0.0005392323,0.0002514777,0.0001474582,0.001337514],"category_scores_gemma":[0.0009533191,0.0001224477,0.0001372782,0.00361297,0.0001114526,0.0004648556,0.0003025696,0.0001664779,0.0005147299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005784999,"about_ca_system_score_gemma":0.0004574371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08744439,"about_ca_topic_score_gemma":0.2645151,"domain_scores_codex":[0.9996114,0.00003534585,0.00004207118,0.00006650114,0.0002024374,0.00004221623],"domain_scores_gemma":[0.998933,0.00006419661,0.0002767103,0.00008779876,0.0005770828,0.00006114757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001311827,0.0001271243,0.893485,0.0001024095,0.0001507827,0.0001599427,0.0003698761,0.002738161,0.002205828,0.0002425771,0.01904572,0.08124145],"study_design_scores_gemma":[0.000002963978,0.00001006566,0.9910546,0.000007266086,0.00001641168,0.00005956029,0.0002086306,0.001787851,0.0003671424,0.00004630153,0.006434257,0.000004893186],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9336731,0.0006718689,0.00203892,0.0001041277,0.00002186911,0.0001080849,0.04892643,0.0003216485,0.01413403],"genre_scores_gemma":[0.8632653,0.0008354578,0.00689446,0.00007182352,0.00003816105,0.0002365366,0.1244225,0.00005126705,0.004184542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08744439,"threshold_uncertainty_score":0.1738708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007627940603117411,"score_gpt":0.2224993312548642,"score_spread":0.2148713906517467,"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."}}