{"id":"W4394533736","doi":"10.6084/m9.figshare.7503599","title":"Mapping Wetlands and Land Cover Change with Landsat Archives: The Added Value of Geomorphologic Data","year":2018,"lang":"en","type":"dataset","venue":"Figshare","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wetland; Land cover; Cover (algebra); Geography; Remote sensing; Physical geography; Environmental science; Land use; Ecology; 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":[0.001010308,0.000622488,0.0003415517,0.003337229,0.0004969202,0.001037788,0.001100182,0.0004738322,0.001731175],"category_scores_gemma":[0.002690927,0.0002334259,0.0004576997,0.004462752,0.0002740308,0.0005706471,0.0006685363,0.0005209426,0.0007430785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00379849,"about_ca_system_score_gemma":0.003100726,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8213024,"about_ca_topic_score_gemma":0.9208781,"domain_scores_codex":[0.9993744,0.00009065245,0.00003263827,0.0001490631,0.0002515848,0.000101587],"domain_scores_gemma":[0.9984457,0.0002206971,0.0001535138,0.0003276775,0.0007413603,0.0001110705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006161176,0.0005843535,0.6208378,0.0007483538,0.0008154538,0.0007126708,0.0006156933,0.03252323,0.006659403,0.001376535,0.1326575,0.201853],"study_design_scores_gemma":[0.0001527045,0.00004970606,0.878863,0.0001679228,0.0001369885,0.0001092259,0.00064301,0.04110424,0.003048695,0.0004091676,0.07523233,0.00008315463],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5036466,0.0007382448,0.004702159,0.0006595879,0.00008044333,0.0003418385,0.4811366,0.001747121,0.006947445],"genre_scores_gemma":[0.3738971,0.0003415664,0.02128583,0.0001004475,0.00003475084,0.0002221824,0.6010268,0.0001598388,0.002931504],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.8213024,"threshold_uncertainty_score":0.3594999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05596868574883344,"score_gpt":0.2493113388023592,"score_spread":0.1933426530535258,"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."}}