{"id":"W4408967690","doi":"10.22541/essoar.172736254.41350153/v3","title":"Computationally efficient subglacial drainage modeling using Gaussian Process emulators: GlaDS-GP v1.0","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Gaussian process; Process (computing); Computer science; Drainage; Geology; Gaussian; Environmental science; Physics; Ecology","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":[0.001017765,0.0008251636,0.0007380529,0.0004288167,0.0004507354,0.0007463881,0.001505268,0.001154302,0.002209089],"category_scores_gemma":[0.003279166,0.0004393328,0.0007671768,0.0004838366,0.0004902334,0.0006685392,0.0007521424,0.001349589,0.0004493767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006785502,"about_ca_system_score_gemma":0.00143518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01646585,"about_ca_topic_score_gemma":0.01198508,"domain_scores_codex":[0.9997932,0.0000741916,0.00001022454,0.00003396561,0.00005240207,0.00003590064],"domain_scores_gemma":[0.9991766,0.0005216282,0.00004655686,0.00007979298,0.0001323145,0.00004312085],"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":[0.00003741532,0.0000416881,0.001559103,0.00002199024,0.00003460668,0.00003702346,0.0000453524,0.9885852,0.000706273,0.001675319,0.0009360386,0.006320021],"study_design_scores_gemma":[0.000008670736,0.000004294467,0.00009793327,0.000001381814,0.000001922034,0.000002795084,0.000004957271,0.9991191,0.0002743913,0.0003082422,0.000173957,0.000002268712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.515265,0.0002231548,0.4625683,0.0004699648,0.0001002862,0.0002029307,0.001755632,0.01025,0.009164729],"genre_scores_gemma":[0.8474898,0.0001114046,0.1467996,0.0001933746,0.00002620568,0.0003460565,0.002204412,0.001079736,0.001749544],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01646585,"threshold_uncertainty_score":0.03274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1804803545329601,"score_gpt":0.4432457240089163,"score_spread":0.2627653694759562,"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."}}