{"id":"W4324142021","doi":"10.5194/tc-17-1225-2023","title":"Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar","year":2023,"lang":"en","type":"article","venue":"˜The œcryosphere","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières; CentrEau - Quebec Water Management Research Centre; Center for Northern Studies","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Snow; Lidar; Snowpack; Vegetation (pathology); Physical geography; Spatial distribution; Spatial variability; Environmental science; Snow field; Geology; Spatial ecology; Context (archaeology); Variogram; Hydrology (agriculture); Remote sensing; Geomorphology; Ecology; Geography; Kriging; Snow cover","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":[0.0001679809,0.0000829806,0.0001376269,0.000006093557,0.0001241454,0.00001114794,0.0001312875,0.00004482131,0.00007288148],"category_scores_gemma":[0.0000328934,0.00005081502,0.00004772287,0.0004074562,0.0001627596,0.00005465216,0.00002164961,0.00007486323,0.00000682679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003956829,"about_ca_system_score_gemma":0.00001183699,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006123989,"about_ca_topic_score_gemma":0.0566808,"domain_scores_codex":[0.999238,0.00006484909,0.0002244518,0.0001243173,0.0001975143,0.0001509361],"domain_scores_gemma":[0.9994981,0.0001887029,0.0001276397,0.0001479041,0.00001317454,0.00002442943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001949466,0.00001371404,0.9761114,0.00001289013,0.00001969283,4.187578e-7,0.0002079197,0.001752798,0.0001383035,0.00003679175,0.0003485351,0.02133807],"study_design_scores_gemma":[0.0003628242,0.00005316554,0.9904298,0.00002431948,0.00001899909,4.699184e-7,0.0002928757,0.006128924,0.00007557007,0.0003218043,0.002235805,0.00005544485],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978337,0.001026169,0.0002581182,0.0002419491,0.0001214906,0.0002611359,0.0001216113,0.000008315118,0.0001275084],"genre_scores_gemma":[0.9994355,0.0002125506,0.00001147603,0.00003635325,0.00001914889,0.000004587444,0.0001415533,0.000002390617,0.0001364716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05055681,"threshold_uncertainty_score":0.9605323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009592331011435102,"score_gpt":0.1942270487518621,"score_spread":0.1846347177404269,"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."}}