{"id":"W4221028823","doi":"10.3390/rs14071649","title":"An Accuracy Assessment of Snow Depth Measurements in Agro-Forested Environments by UAV Lidar","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Trois-Rivières; Center for Northern Studies","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Snow; Remote sensing; Environmental science; Point cloud; Meteorology; Geology; Geography; Computer science","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":[0.0004434145,0.0002139087,0.0001511927,0.0004965086,0.0003560757,0.0004172808,0.0004021334,0.0001579392,0.0004156718],"category_scores_gemma":[0.0009926051,0.00009056908,0.00007961833,0.0006700449,0.0001681707,0.0002792359,0.0001977363,0.00009666416,0.0001290353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009983563,"about_ca_system_score_gemma":0.0007890414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2884959,"about_ca_topic_score_gemma":0.5151252,"domain_scores_codex":[0.9997253,0.00004282004,0.00001238927,0.00004470084,0.0001305706,0.00004416211],"domain_scores_gemma":[0.9995461,0.00007369865,0.00005453688,0.00003235165,0.000271335,0.00002192836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003282735,0.00008139704,0.7572048,0.0001517828,0.00008712513,0.0002717807,0.0009054959,0.02003386,0.09303379,0.0001833218,0.0005077955,0.1272106],"study_design_scores_gemma":[0.00002271141,0.0002061886,0.912923,0.00004096424,0.00004563184,0.0001648326,0.001248423,0.06501343,0.01835602,0.00006882565,0.001877833,0.00003205908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949297,0.0002104118,0.003240645,0.00001585782,0.000003803483,0.00001593173,0.0002606004,0.00006389374,0.001259123],"genre_scores_gemma":[0.9961417,0.00007542634,0.003442777,0.000007331387,0.000001129165,0.000004559927,0.0001509686,0.000004567477,0.0001714945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2884959,"threshold_uncertainty_score":0.5736333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0434584512705577,"score_gpt":0.2716018144299812,"score_spread":0.2281433631594235,"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."}}