{"id":"W2288668267","doi":"10.5194/tc-10-2559-2016","title":"Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle","year":2016,"lang":"en","type":"article","venue":"˜The œcryosphere","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":155,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Strong; University of Calgary","keywords":"Snow; Snowpack; Remote sensing; Terrain; Environmental science; Snow field; Elevation (ballistics); Geology; Physical geography; Snow cover; Geomorphology; Geography; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001709437,0.000105665,0.0001296833,0.000005386034,0.000128477,0.0000235387,0.000146374,0.00004884429,0.000684045],"category_scores_gemma":[0.0000567367,0.00006319107,0.0000200271,0.00011491,0.0001304594,0.0002875762,0.00002009879,0.00004979868,0.00003273727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008743837,"about_ca_system_score_gemma":0.00001857088,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003803911,"about_ca_topic_score_gemma":0.03811168,"domain_scores_codex":[0.9991823,0.00006342618,0.0002095966,0.0001882063,0.0001609155,0.0001955608],"domain_scores_gemma":[0.9993528,0.0002871965,0.0001092662,0.0001854521,0.00000914063,0.00005618899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000652014,0.00003578133,0.7352144,0.000005746557,0.00001384836,0.000001183861,0.0002947121,0.001078565,0.0004247425,0.00003344193,0.001671114,0.2611613],"study_design_scores_gemma":[0.000633624,0.0001312498,0.9760942,0.00001958779,0.00001015658,0.000001309786,0.0003409272,0.009418556,0.0001485494,0.0005621596,0.01253006,0.0001096168],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996883,0.0006088366,0.001118357,0.0007992224,0.000105719,0.0002094893,0.00006629573,0.00001213258,0.0001969304],"genre_scores_gemma":[0.9988659,0.0001176918,0.0004312144,0.0001062592,0.0000362276,0.00000431613,0.0000352163,0.000004091787,0.0003990855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2610517,"threshold_uncertainty_score":0.9794403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01341297515397403,"score_gpt":0.2256113420926248,"score_spread":0.2121983669386508,"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."}}