{"id":"W2031839767","doi":"10.4296/cwrj3702893","title":"Spatial Snow Depth Assessment Using LiDAR Transect Samples and Public GIS Data Layers in the Elbow River Watershed, Alberta","year":2012,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Government of Alberta; ARC Resources (Canada); Acadia University; Wilfrid Laurier University","funders":"Government of Alberta","keywords":"Snow; Lidar; Snowpack; Watershed; Environmental science; Hydrology (agriculture); Elevation (ballistics); Terrain; Transect; Snowmelt; Remote sensing; Sampling (signal processing); Geology; Geography; Geomorphology; Filter (signal processing); Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001496692,0.0003543703,0.0003391827,0.0003354908,0.001172484,0.0005891651,0.001203582,0.0001548772,0.0002205858],"category_scores_gemma":[0.0001078206,0.0002429718,0.00008604534,0.0003091219,0.0008111164,0.0007996254,0.0001392661,0.0006060702,0.00001757388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009679692,"about_ca_system_score_gemma":0.0000132428,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8447564,"about_ca_topic_score_gemma":0.9718069,"domain_scores_codex":[0.9965347,0.0005673978,0.0004848067,0.0005263348,0.0002454334,0.001641333],"domain_scores_gemma":[0.9974289,0.0001617753,0.0001424223,0.0008189394,0.00002883789,0.001419159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002112214,0.00005952635,0.3339273,0.00003909042,0.00009775485,0.0001987901,0.6274177,0.0006253895,0.002460151,0.000005199478,0.0001152063,0.03503276],"study_design_scores_gemma":[0.0003606047,0.00007575961,0.1196489,0.0001161932,0.00009084127,0.002524988,0.003766528,0.003921581,0.0002965934,0.000279404,0.8683452,0.0005734279],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994691,0.0002904901,0.0002502457,0.00229123,0.00009808016,0.0002971299,0.00005854397,0.00001568894,0.002007614],"genre_scores_gemma":[0.9972557,0.0001488674,0.001464082,0.0004843566,0.0003676307,0.00000493143,0.00009707614,0.0000535206,0.0001238574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.86823,"threshold_uncertainty_score":0.9908101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04677140495787563,"score_gpt":0.2448878376989816,"score_spread":0.198116432741106,"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."}}