{"id":"W1964226953","doi":"10.5194/tc-9-411-2015","title":"Large-area land surface simulations in heterogeneous terrain driven by global data sets: application to mountain permafrost","year":2015,"lang":"en","type":"article","venue":"The cryosphere","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Terrain; Permafrost; Snow; Context (archaeology); Snowpack; Environmental science; Precipitation; Geology; Forcing (mathematics); Climate model; Bedrock; Meteorology; Climatology; Climate change; 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":[0.0006257163,0.0004527177,0.0004311999,0.0003797844,0.0004397451,0.0005066296,0.0007240208,0.0008101009,0.000998725],"category_scores_gemma":[0.00156509,0.0001933068,0.0005047038,0.0007193691,0.0005879612,0.0004573235,0.0004640051,0.0005353507,0.00008875592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006180421,"about_ca_system_score_gemma":0.0005959172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04125649,"about_ca_topic_score_gemma":0.02660651,"domain_scores_codex":[0.9998268,0.00008208748,0.000010065,0.00002897494,0.00002313737,0.00002903092],"domain_scores_gemma":[0.9990043,0.0006063955,0.00008411712,0.0001055547,0.0001079462,0.00009180395],"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":[0.00007860081,0.000122267,0.01409868,0.00001654598,0.00004376138,0.000106352,0.00004295688,0.9818481,0.001149227,0.0002620839,0.0001675516,0.002063911],"study_design_scores_gemma":[0.00004588208,0.00003905129,0.003955062,0.000001839939,0.000005125962,0.000006188553,0.00002651116,0.9953495,0.0003764323,0.0001212326,0.00006883394,0.000004492888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996696,0.00003034855,0.002206237,0.00008653466,0.000008358717,0.00001171028,0.0002567429,0.0001383813,0.0005658237],"genre_scores_gemma":[0.9968182,0.00001484945,0.00271587,0.00001561903,0.000004803755,0.00001114075,0.0003007037,0.00001633106,0.0001024196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04125649,"threshold_uncertainty_score":0.08203268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04209185441249473,"score_gpt":0.283082782566227,"score_spread":0.2409909281537322,"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."}}