{"id":"W2120833021","doi":"10.1002/hyp.6930","title":"Modelling longwave radiation to snow beneath forest canopies using hemispherical photography or linear regression","year":2008,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Foundation for Climate and Atmospheric Sciences; National Oceanic and Atmospheric Administration; Natural Environment Research Council; University of Calgary","keywords":"Environmental science; Longwave; Shortwave radiation; Canopy; Shortwave; Snow; Atmospheric sciences; Snowmelt; Radiometer; Remote sensing; Tree canopy; Radiative transfer; Meteorology; Radiation; Geography; Geology","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":[0.0002404961,0.0005638352,0.0003200181,0.0004155585,0.0002763678,0.0005832465,0.0007274817,0.0005841416,0.002404526],"category_scores_gemma":[0.0005262508,0.0003207762,0.0008260328,0.0006490713,0.000233409,0.0003112591,0.0001992724,0.0002914234,0.0004292293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001511934,"about_ca_system_score_gemma":0.0009538502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3255389,"about_ca_topic_score_gemma":0.2178911,"domain_scores_codex":[0.9998672,0.00002359798,0.000005732646,0.00004714622,0.00002534597,0.00003095683],"domain_scores_gemma":[0.9998595,0.00007476156,0.00002009062,0.00000807246,0.00002675115,0.00001083369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003978722,0.0000273077,0.01242206,0.00002756475,0.00004921441,0.00004539911,0.00002833687,0.9776953,0.00265354,0.0001533033,0.0001518714,0.006706326],"study_design_scores_gemma":[0.000004619774,0.0000134285,0.01150323,0.000003139378,0.00001247007,0.000009759609,0.00002676109,0.9874811,0.0006637231,0.00007606328,0.0001978886,0.000007941017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9424378,0.0002019463,0.0515355,0.00006917566,0.00001901685,0.00003880799,0.001124259,0.0006530516,0.003920468],"genre_scores_gemma":[0.991592,0.00008445753,0.005537736,0.00001289178,0.000004588403,0.00002444029,0.0004033409,0.00005665892,0.002283772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3255389,"threshold_uncertainty_score":0.6472881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08737037926746805,"score_gpt":0.2599683535908258,"score_spread":0.1725979743233578,"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."}}