{"id":"W3126324944","doi":"10.3390/rs13040599","title":"Multi-Source EO for Dynamic Wetland Mapping and Monitoring in the Great Lakes Basin","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Environment and Climate Change Canada","funders":"U.S. Fish and Wildlife Service","keywords":"Wetland; Environmental science; Environmental resource management; Structural basin; Remote sensing; Environmental monitoring; Constellation; Vegetation (pathology); Hydrology (agriculture); Water resource management; Geography; Ecology; Geology; Environmental engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002885788,0.0001144957,0.0001269543,0.00002989712,0.0002602547,0.00008783212,0.00006725555,0.00005619372,0.000003148677],"category_scores_gemma":[0.00008223449,0.00009593054,0.0000425164,0.0002500747,0.00007831948,0.00005525253,0.00005788117,0.0001316781,0.00001206616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000782029,"about_ca_system_score_gemma":0.000008058492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003742379,"about_ca_topic_score_gemma":0.0005087891,"domain_scores_codex":[0.9990777,0.00008099479,0.0001564074,0.0003100538,0.0001329091,0.0002419077],"domain_scores_gemma":[0.9994515,0.000187818,0.0000441429,0.0002640658,0.00001038945,0.0000420873],"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":[0.000005223348,0.00001453354,0.002439687,0.00001980264,0.00000810753,0.00002619339,0.003319364,0.001214305,0.233492,0.000001684869,0.00006191521,0.7593972],"study_design_scores_gemma":[0.000602673,0.00001177551,0.05814671,0.0001737645,0.00002323268,0.0003513883,0.00318891,0.9036759,0.005297365,0.0002762787,0.02797078,0.0002811815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9230148,0.0001068228,0.07454764,0.001114861,0.00007282184,0.0001951402,0.000001278374,0.00003179732,0.000914863],"genre_scores_gemma":[0.8914973,0.00004623962,0.1076429,0.0001332612,0.00005706868,3.234167e-8,0.000005126313,0.00001762248,0.0006005345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9024616,"threshold_uncertainty_score":0.3911934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0203896518279223,"score_gpt":0.2562030388926093,"score_spread":0.235813387064687,"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."}}