{"id":"W6966338467","doi":"10.4121/21127528.v1","title":"Wetland Classification with Deep ResU-Net Convolutional Neural Network and Multitemporal Sentinel-1 & 2 Imagery and ALOS Elevation Data: A Case Study in Alberta Parkland & Grassland Natural Region, Canada","year":2022,"lang":"en","type":"dataset","venue":"4TU.ResearchData","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Elevation (ballistics); Wetland; Random forest; Convolutional neural network; Satellite; Satellite imagery; Grassland","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003511803,0.001050136,0.001113132,0.0008950389,0.001165501,0.0008172372,0.001740566,0.0002920753,0.0003813641],"category_scores_gemma":[0.001975381,0.0009648643,0.00004141773,0.001786304,0.0007460564,0.001553199,0.003624927,0.003092093,0.00002595437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007831301,"about_ca_system_score_gemma":0.002076879,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9115113,"about_ca_topic_score_gemma":0.9979421,"domain_scores_codex":[0.9887485,0.002727175,0.001310909,0.002980961,0.002611001,0.001621443],"domain_scores_gemma":[0.991745,0.00214454,0.0008500088,0.004255638,0.0004400411,0.0005647816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001515483,0.0003164435,0.261274,0.0002001844,0.0002654725,0.004567318,0.00008825448,0.00009129313,0.000007688305,0.000001800166,0.7316253,0.00004684023],"study_design_scores_gemma":[0.005774577,0.0001280846,0.1569474,0.0001249146,0.0002949213,0.006676208,0.0006288972,0.009587544,6.629703e-8,0.00001346259,0.8186276,0.001196351],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.261741,0.0009735259,0.000006886513,0.0005641204,0.0002540249,0.002748098,0.733667,0.00003962962,0.000005652213],"genre_scores_gemma":[0.118236,0.0002539782,0.00009620239,0.00009348921,0.0004661889,0.0003809218,0.8799058,0.0001548708,0.0004125614],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1462388,"threshold_uncertainty_score":0.9992802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09349743064957579,"score_gpt":0.3367917454669007,"score_spread":0.2432943148173249,"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."}}