{"id":"W1997764658","doi":"10.3189/172756506781828584","title":"Calculating ice melt beneath a debris layer using meteorological data","year":2006,"lang":"en","type":"article","venue":"Journal of Glaciology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":457,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of St Andrews; Carnegie Trust for the Universities of Scotland; Royal Scottish Geographical Society; Royal Society; Quaternary Research Association","keywords":"Debris; Geology; Glacier; Glacier mass balance; Surface runoff; Climatology; Atmospheric sciences; Geomorphology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114206,0.0002989709,0.0001983417,0.0003939416,0.0001710203,0.0003644686,0.0002585304,0.0002578571,0.0006533833],"category_scores_gemma":[0.0006524405,0.0001643027,0.0003753153,0.0002992983,0.000113019,0.0003602538,0.000190273,0.0001579906,0.0001617505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003921249,"about_ca_system_score_gemma":0.0004072346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01268791,"about_ca_topic_score_gemma":0.01063098,"domain_scores_codex":[0.9999408,0.00001082289,0.000005845149,0.0000182282,0.00001386279,0.0000105409],"domain_scores_gemma":[0.9997998,0.00006668026,0.00003974024,0.00003366611,0.00004191182,0.00001818639],"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":[0.0001282866,0.00003897763,0.0975407,0.00005940342,0.0001053105,0.0001317251,0.00008329339,0.8630514,0.01575174,0.0006978595,0.0002586105,0.02215281],"study_design_scores_gemma":[0.00002029779,0.00003432978,0.03832259,0.000007559599,0.0000165808,0.00003264347,0.00002584497,0.9554979,0.005035943,0.0004958291,0.0004977785,0.00001268088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722937,0.0000709419,0.02531192,0.00002246702,0.000008935051,0.00001792152,0.0006662066,0.0003587156,0.00124919],"genre_scores_gemma":[0.9938239,0.00003660849,0.005511129,0.00000379806,0.000003273813,0.0000114395,0.0003134318,0.00001778246,0.0002784962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01268791,"threshold_uncertainty_score":0.02522814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1070726916211567,"score_gpt":0.2952532592734322,"score_spread":0.1881805676522756,"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."}}