{"id":"W2471657663","doi":"10.1080/07038992.2016.1196583","title":"A Physically Based Terrain Morphology and Vegetation Structural Classification for Wetlands of the Boreal Plains, Alberta, Canada","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Lethbridge","funders":"","keywords":"Wetland; Lidar; Terrain; Vegetation (pathology); Remote sensing; Land cover; Taiga; Boreal; Environmental science; Physical geography; Geography; Multispectral image; Land use; Hydrology (agriculture); Cartography; Forestry; Geology; Ecology; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005112981,0.0003741314,0.0001741814,0.001118754,0.001079105,0.001085031,0.0005614234,0.0001859445,0.001022699],"category_scores_gemma":[0.001282357,0.0001981984,0.0001834764,0.0007234907,0.00043876,0.0003327708,0.0003787971,0.0001676318,0.0002262902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005532069,"about_ca_system_score_gemma":0.005865161,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9219385,"about_ca_topic_score_gemma":0.9758716,"domain_scores_codex":[0.999616,0.00003939066,0.00001747223,0.00006623201,0.0002110859,0.00004974382],"domain_scores_gemma":[0.9993593,0.00008143737,0.00006855184,0.00003284388,0.0003932232,0.00006470686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005010368,0.0003829953,0.7967719,0.0001101223,0.00005795217,0.0002306829,0.0007712637,0.01143715,0.02095258,0.0004328857,0.001672195,0.1666793],"study_design_scores_gemma":[0.00002939906,0.0001071281,0.9739845,0.00001574942,0.00002078425,0.00009616451,0.0008630027,0.02162361,0.002036182,0.00007686927,0.001128773,0.00001777666],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995001,0.0000745797,0.002376549,0.00003847523,0.000003743784,0.0001121731,0.0006454468,0.00007833729,0.001669543],"genre_scores_gemma":[0.9773799,0.00009479903,0.01916721,0.00002512015,0.000003053716,0.00005257474,0.001630135,0.00001580291,0.001631573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07806146,"threshold_uncertainty_score":0.1570423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008312959570007678,"score_gpt":0.2069783796663048,"score_spread":0.1986654200962971,"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."}}