{"id":"W4249030513","doi":"10.5194/tc-2018-82","title":"Monitoring snow depth change across a range of landscapes with ephemeral snow packs using Structure from Motion applied to lightweight unmanned aerial vehicle videos","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Natural Resources Canada","funders":"Natural Resources Canada; Public Safety Canada","keywords":"Snow; Digital elevation model; Snowpack; Remote sensing; Ephemeral key; Photogrammetry; Elevation (ballistics); Geology; Range (aeronautics); Vegetation (pathology); Structure from motion; Environmental science; Geomorphology; Motion (physics); Artificial intelligence; Computer science; Geometry; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001699758,0.0001630827,0.0001249647,0.0009302793,0.0001657757,0.0002650812,0.0001592483,0.0001731861,0.0002974694],"category_scores_gemma":[0.0003631837,0.00007009828,0.0001473883,0.0006954412,0.0001025971,0.0003381601,0.0002017821,0.00008801499,0.00008511129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002131872,"about_ca_system_score_gemma":0.0001142967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009727272,"about_ca_topic_score_gemma":0.02350009,"domain_scores_codex":[0.9999081,0.00001583746,0.00000534084,0.00003277419,0.00002595106,0.00001198471],"domain_scores_gemma":[0.9998283,0.00003125607,0.0000462499,0.00002388346,0.00005020701,0.00002005579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003216054,0.000290069,0.8565067,0.0001117423,0.0001932407,0.0002162085,0.0005099407,0.0178652,0.03873769,0.0001901689,0.0006518463,0.08440564],"study_design_scores_gemma":[0.000008744058,0.00008694288,0.9718319,0.000008217981,0.00002295982,0.00004677053,0.0001891436,0.02445592,0.002700172,0.00008057582,0.0005624319,0.000006166622],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979029,0.00005392206,0.00114832,0.00000693629,0.00000243925,0.00001186689,0.0004351196,0.00002993736,0.0004084802],"genre_scores_gemma":[0.996992,0.00003257752,0.002188427,0.000003721344,0.00000330587,0.000007572495,0.0006501086,0.000005223355,0.0001171114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009727272,"threshold_uncertainty_score":0.01934129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04054453298592148,"score_gpt":0.2595699074886919,"score_spread":0.2190253745027704,"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."}}