{"id":"W3214950806","doi":"10.3390/rs13234745","title":"Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Biodiversity Monitoring Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Biodiversity Monitoring Institute; University of Alberta; University of Calgary","keywords":"Environmental science; Workflow; Satellite; Remote sensing; Meteorology; Computer science; Earth observation; Environmental resource management; Geography; Database; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002801263,0.0001323722,0.0001870389,0.00001649412,0.000129305,0.00005068719,0.00006292984,0.00005486936,0.00004095454],"category_scores_gemma":[0.0001289265,0.0001053247,0.00004162619,0.0002135969,0.00006355972,0.0002388926,0.0001107395,0.0001062175,0.00004551596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005301532,"about_ca_system_score_gemma":0.00001523905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001825738,"about_ca_topic_score_gemma":0.0003251949,"domain_scores_codex":[0.9989848,0.00009218651,0.0001828744,0.0002388775,0.0002438937,0.0002574061],"domain_scores_gemma":[0.9994359,0.0001482071,0.00008848014,0.000252591,0.00001779408,0.00005701492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001025757,0.00005514585,0.07475722,0.0003693709,0.0001199132,0.0002460626,0.002970577,0.00364828,0.4030873,0.00001534142,0.0001265264,0.5145017],"study_design_scores_gemma":[0.0008068929,0.0001621433,0.28852,0.0009902497,0.00008119087,0.0006101738,0.0004894013,0.3163457,0.3853546,0.0009720587,0.005006247,0.0006614286],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996354,0.0004050202,0.0004588267,0.0001061057,0.0001598879,0.0001510602,0.000003933753,0.00003275112,0.002328405],"genre_scores_gemma":[0.9962099,0.00007090384,0.002688972,0.00003042663,0.00008682835,3.431308e-8,0.000002996283,0.00002229717,0.0008876381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5138403,"threshold_uncertainty_score":0.4295017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003773457010230612,"score_gpt":0.2009413773159422,"score_spread":0.1971679203057116,"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."}}