{"id":"W4403993149","doi":"10.5194/isprs-archives-xlviii-3-w3-2024-45-2024","title":"Rapid Post-Wildfire Burned Vegetation Assessment with Google Earth Engine (Case Study: 2023 Canada Wildfires)","year":2024,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vegetation (pathology); Environmental science; Physical geography; Geography; Forestry; Medicine","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":[0.0003505473,0.0006088246,0.0002179472,0.001121444,0.000609363,0.0005489162,0.0004920482,0.0002378302,0.0007221007],"category_scores_gemma":[0.0003584917,0.0001249456,0.0003506372,0.001036128,0.0002260394,0.0002672779,0.0003994411,0.000283954,0.0001999134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00250576,"about_ca_system_score_gemma":0.002369769,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7196313,"about_ca_topic_score_gemma":0.893526,"domain_scores_codex":[0.9997928,0.00001102558,0.000007029869,0.00003814654,0.00009062926,0.00006043783],"domain_scores_gemma":[0.9997513,0.00001603058,0.00001791761,0.00001605305,0.0001595801,0.00003923977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005193941,0.0002663711,0.7321235,0.0002228044,0.0001721101,0.00181694,0.0007460799,0.07615841,0.01715777,0.0009551225,0.008640102,0.1612213],"study_design_scores_gemma":[0.00003198447,0.0001310597,0.5995135,0.00005589811,0.00008566103,0.0003268364,0.002084337,0.3813868,0.01136059,0.0004345131,0.004523237,0.00006547859],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856197,0.0001495356,0.00632244,0.0001078879,0.00001688201,0.0001177088,0.003893828,0.0004399536,0.003331995],"genre_scores_gemma":[0.9843553,0.00008164912,0.0122084,0.00001784581,0.000002513377,0.00001595098,0.002278584,0.00001623431,0.001023624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2803687,"threshold_uncertainty_score":0.5640398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009094124551685045,"score_gpt":0.2345081895538723,"score_spread":0.2254140650021873,"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."}}