{"id":"W2045381707","doi":"10.1007/s11220-006-0020-9","title":"Influence of Snow Temperature Interpolation Algorithm and Dielectric Mixing-Model Coefficient on Density and Liquid Water Content Determination in a Cold Seasonal Snow Pack","year":2006,"lang":"en","type":"article","venue":"Subsurface Sensing Technologies and Applications","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; Institut National de la Recherche Scientifique","funders":"","keywords":"Snow; Approximation error; Environmental science; Liquid water content; Interpolation (computer graphics); Meteorology; Atmospheric sciences; Coefficient of determination; Soil science; Mathematics; Statistics; Geology; Geography; Physics; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001098852,0.0001196023,0.0001539392,0.00006216467,0.0002262929,0.00003790872,0.00004972138,0.0001003895,7.769921e-7],"category_scores_gemma":[0.00004092485,0.00009222541,0.00001458384,0.000254911,0.0001962486,0.00007224647,0.00003440967,0.0001276778,6.04906e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000950447,"about_ca_system_score_gemma":0.0000103806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005399146,"about_ca_topic_score_gemma":0.0013173,"domain_scores_codex":[0.9992651,0.00001372678,0.0001867355,0.0002594074,0.0001012136,0.000173851],"domain_scores_gemma":[0.9995696,0.0001316279,0.00006807202,0.000123903,0.00008676937,0.00002004986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001866735,0.0002248414,0.2693719,0.0001391832,0.00004650549,0.000009436744,0.001013897,0.097956,0.100758,0.004931737,0.0001736476,0.5251881],"study_design_scores_gemma":[0.0003174612,0.0001879018,0.3309826,0.00008857406,0.00002129659,0.00001306059,0.001019728,0.63331,0.03262971,0.00102637,0.000176403,0.0002268258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930501,0.0006668593,0.005113576,0.0007001697,0.000008979743,0.0003401765,0.00002871862,0.00007186417,0.00001953608],"genre_scores_gemma":[0.9933833,0.0003181137,0.006198579,0.00003834494,0.000006407099,0.000003744044,0.00002980628,0.000003023922,0.00001862144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.535354,"threshold_uncertainty_score":0.3760843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01105145485886015,"score_gpt":0.2053579626775728,"score_spread":0.1943065078187126,"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."}}