{"id":"W2564294368","doi":"10.3390/w9010019","title":"Use of Bathymetric and LiDAR Data in Generating Digital Elevation Model over the Lower Athabasca River Watershed in Alberta, Canada","year":2017,"lang":"en","type":"article","venue":"Water","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas; University of Calgary","funders":"University of Alberta","keywords":"Bathymetry; Digital elevation model; Hydrology (agriculture); Watershed; Environmental science; Elevation (ballistics); Inverse distance weighting; Lidar; Geology; Remote sensing; Multivariate interpolation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0002209128,0.0003479407,0.0001951528,0.0008906196,0.0005354114,0.0007899134,0.0008162347,0.0003023588,0.0009138471],"category_scores_gemma":[0.000896762,0.0002062322,0.0003655942,0.001129242,0.0002618431,0.0001996313,0.0003282349,0.0002823319,0.000158302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00450905,"about_ca_system_score_gemma":0.005135602,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8968961,"about_ca_topic_score_gemma":0.9283472,"domain_scores_codex":[0.9998426,0.00001569271,0.00001221348,0.00003731331,0.0000557069,0.0000364468],"domain_scores_gemma":[0.9997727,0.00005349559,0.0000200862,0.00001731135,0.0001074007,0.000028938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001058677,0.0001368864,0.08599923,0.00005857657,0.00005227008,0.0003997529,0.0002814594,0.8456453,0.004304906,0.0006977474,0.001251373,0.06106663],"study_design_scores_gemma":[0.00002085462,0.0000117036,0.03850745,0.00001166151,0.00001893715,0.00002301582,0.0003147376,0.958636,0.001134743,0.0001407635,0.001156805,0.00002323729],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844206,0.00008218773,0.009314716,0.0001297848,0.00001069612,0.00007558733,0.003050331,0.000493845,0.002422279],"genre_scores_gemma":[0.9819164,0.00006783274,0.01384935,0.0000151768,0.000001965525,0.00002603241,0.003309238,0.00003356495,0.0007803972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1031039,"threshold_uncertainty_score":0.2074222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02469847366182367,"score_gpt":0.2240443676919826,"score_spread":0.1993458940301589,"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."}}