{"id":"W3019635997","doi":"10.3390/rs12081321","title":"Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Coastal wetland ecosystem dynamics","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Ducks Unlimited Canada; Environment and Climate Change Canada; Carleton University; University of Alberta; Alberta Environment and Protected Areas; University of Lethbridge","funders":"Alberta Environment and Parks; University of Lethbridge","keywords":"Wetland; Remote sensing; Environmental science; Boreal; Pixel; Sampling (signal processing); Change detection; Taiga; Field (mathematics); Computer science; Hydrology (agriculture); Geography; Ecology; Geology; Artificial intelligence; Mathematics; Forestry","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001218674,0.0002599719,0.000613923,0.00006875172,0.0001281516,0.00002819452,0.0001128183,0.0001067638,0.000007087521],"category_scores_gemma":[0.0003124463,0.0002516062,0.0001270593,0.0002980877,0.00009250639,0.0001482667,0.0002867346,0.0001390509,0.000003610154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001279464,"about_ca_system_score_gemma":0.00002243709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002468465,"about_ca_topic_score_gemma":0.002188172,"domain_scores_codex":[0.9978381,0.0002697824,0.0006370352,0.0004992465,0.0003325112,0.000423365],"domain_scores_gemma":[0.9985788,0.0003673463,0.000495464,0.0002760985,0.00007205744,0.0002101759],"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":[0.0001414796,0.000003743161,0.0008782388,0.0004112271,0.00004334038,0.000009482983,0.002943618,0.0007519528,0.04737139,0.00000150337,0.00002539995,0.9474186],"study_design_scores_gemma":[0.0008227217,0.0002502103,0.002278821,0.000283369,0.0000844696,0.00005965106,0.0004138008,0.9897806,0.005173388,0.0002798921,0.0003247597,0.0002483058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7005203,0.0000292957,0.2973047,0.0002457971,0.0001164678,0.0005787959,0.00005761268,0.00003275918,0.001114208],"genre_scores_gemma":[0.6600701,0.00003291302,0.3397148,0.00006279228,0.00005159711,1.441013e-8,0.00001965239,0.00003373979,0.00001442329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9890286,"threshold_uncertainty_score":0.9999936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05451233808477883,"score_gpt":0.3172931756545777,"score_spread":0.2627808375697989,"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."}}