{"id":"W4234557978","doi":"10.24124/2015/bpgub1084","title":"Simulating past and future mass balance of Place Glacier using a physically-based, distributed glacier mass balance model","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Glacier mass balance; Glacier; Downscaling; Precipitation; Climatology; Environmental science; Mean squared error; Snow; Climate change; Balance (ability); Climate model; Atmospheric sciences; Meteorology; Geography; Physical geography; Geology; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001879906,0.0006435571,0.0003816568,0.0004149622,0.0005946579,0.000727936,0.001147905,0.0006731607,0.001708278],"category_scores_gemma":[0.000425547,0.0003207579,0.0004603901,0.0005435062,0.0004354228,0.0005031902,0.0003291172,0.0007131124,0.0001978768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002831531,"about_ca_system_score_gemma":0.004449074,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5444211,"about_ca_topic_score_gemma":0.4896472,"domain_scores_codex":[0.9999293,0.000009229906,0.000002929667,0.0000249623,0.00001856922,0.00001495986],"domain_scores_gemma":[0.9998577,0.00002704835,0.0000178152,0.0000107589,0.00006368027,0.00002308827],"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":[0.00001474224,0.00001761214,0.003866079,0.000008377964,0.00001309895,0.0000258347,0.00001474871,0.9935801,0.0006508439,0.0001967057,0.0001659289,0.001445818],"study_design_scores_gemma":[0.00002508741,0.000009854509,0.003092164,0.000002326404,0.000009901668,0.000006183873,0.0000146367,0.9961762,0.0002067498,0.0001329886,0.0003180912,0.000005742731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9647962,0.0001827299,0.02248263,0.0002694211,0.00003520151,0.00007165845,0.002190449,0.0006981582,0.00927347],"genre_scores_gemma":[0.9917463,0.00007479898,0.005199142,0.00002079213,0.000008321458,0.00003894315,0.000983939,0.00003130964,0.00189642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5444211,"threshold_uncertainty_score":0.9165238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0196408969756514,"score_gpt":0.2501625122952544,"score_spread":0.230521615319603,"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."}}