{"id":"W3161837918","doi":"10.1007/s11269-021-02948-7","title":"Assessing Optimal Digital Elevation Model Selection for Active River Area Delineation Across Broad Regions","year":2021,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nature Conservancy of Canada; Dalhousie University","funders":"","keywords":"Digital elevation model; Smoothing; Lidar; Elevation (ballistics); Riparian zone; Computer science; Remote sensing; Geographic information system; Data mining; Environmental science; Geography; Mathematics; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.002810474,0.0004858197,0.0005879578,0.001281299,0.000356262,0.00108103,0.0007263899,0.0007866293,0.001023087],"category_scores_gemma":[0.01070741,0.0004032047,0.0006143109,0.0008932307,0.0003783201,0.0009438593,0.0007758995,0.0004167111,0.0001587671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008822788,"about_ca_system_score_gemma":0.001361017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01517425,"about_ca_topic_score_gemma":0.01726887,"domain_scores_codex":[0.9993722,0.0003292515,0.00004613948,0.0001087934,0.00007224615,0.00007147061],"domain_scores_gemma":[0.9936283,0.005346738,0.0002367428,0.0002711822,0.0003613508,0.0001555426],"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":[0.0003796748,0.0001469549,0.02951056,0.00003852981,0.0001002256,0.00005750399,0.00007675821,0.9302151,0.001141105,0.0008474627,0.0003765679,0.03710958],"study_design_scores_gemma":[0.00004751045,0.0000499409,0.003869917,0.000005658487,0.00003159113,0.00001430101,0.00006249666,0.9944646,0.0008985656,0.0004331379,0.0001173187,0.000004886525],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9473575,0.0001539591,0.05037454,0.0001860066,0.00001202745,0.00005179449,0.0004059161,0.0004175245,0.00104081],"genre_scores_gemma":[0.9788941,0.00003880137,0.0204562,0.00002377809,0.000004196026,0.00001808088,0.0003670649,0.00002681913,0.0001710872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01517425,"threshold_uncertainty_score":0.03017181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02158244700103642,"score_gpt":0.2609304974179533,"score_spread":0.2393480504169169,"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."}}