{"id":"W3175996434","doi":"","title":"Wetland Inventory of Canada using Satellite Earth Observation Data and Google Earth Engine Cloud","year":2019,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Earth (classical element); Cloud computing; Earth observation; Earth observation satellite; Satellite; Remote sensing; Environmental science; Wetland; Meteorology; Geography; Computer science; Engineering","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":[0.0002616285,0.0004802109,0.0003600387,0.004709657,0.001442693,0.00107585,0.0008271572,0.0002164947,0.003374258],"category_scores_gemma":[0.0007005752,0.0003231791,0.0005119082,0.009378221,0.0002757543,0.0004649007,0.0005702961,0.0003630551,0.0007859977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02161075,"about_ca_system_score_gemma":0.05288243,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9989998,"about_ca_topic_score_gemma":0.9993049,"domain_scores_codex":[0.9996697,0.00001050485,0.00002119717,0.00004539709,0.0001687003,0.00008445127],"domain_scores_gemma":[0.9984441,0.00003634226,0.00009810007,0.00002996786,0.001184755,0.0002067857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005835107,0.0002391892,0.7023591,0.0009818686,0.0006429103,0.0005164825,0.001096899,0.008900904,0.003717458,0.002934003,0.1745995,0.1034282],"study_design_scores_gemma":[0.00004064568,0.00002159351,0.9494308,0.0001427016,0.0001235782,0.00008322488,0.001014836,0.006276485,0.0008286134,0.0001614919,0.04182575,0.00005020316],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4132927,0.001819764,0.001583778,0.0006032775,0.00006551673,0.0002052346,0.5640834,0.0007727544,0.01757345],"genre_scores_gemma":[0.7332746,0.002809815,0.006651862,0.0002904951,0.0000230116,0.0001669924,0.2351891,0.0001628213,0.02143137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02161075,"threshold_uncertainty_score":0.1567977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02393882075775123,"score_gpt":0.2202370903493284,"score_spread":0.1962982695915771,"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."}}