{"id":"W4388224759","doi":"10.5194/egusphere-2023-2491","title":"A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.10) Artificial Neural Networks","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Post-glacial rebound; Geodetic datum; Artificial neural network; Earth model; Geology; Geodesy; Glacial period; Computer science; Artificial intelligence; Algorithm; Geophysics; Geomorphology","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":[0.001066471,0.0006843357,0.0005175617,0.0004431338,0.0003971993,0.0008452378,0.0009807934,0.001138058,0.00182988],"category_scores_gemma":[0.002493443,0.0003807823,0.0007732327,0.0004071027,0.0004756212,0.0007025289,0.000648043,0.001096765,0.0002352457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048017,"about_ca_system_score_gemma":0.001223621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01896327,"about_ca_topic_score_gemma":0.01087709,"domain_scores_codex":[0.9997916,0.00007811836,0.00001362721,0.00003901173,0.00005038642,0.00002727507],"domain_scores_gemma":[0.9991289,0.0004237872,0.0001018299,0.00005425779,0.0002416777,0.00004956169],"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":[0.00001567959,0.00001125407,0.0003089441,0.00000592975,0.000006921436,0.00000903043,0.000004495903,0.9964166,0.0002058964,0.0006327039,0.0001036386,0.002278981],"study_design_scores_gemma":[7.876814e-7,0.000002270647,0.00001979605,5.094205e-7,4.442504e-7,6.156029e-7,2.84147e-7,0.9998363,0.00003738884,0.00007556025,0.00002529137,6.771981e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2390316,0.0003176995,0.7503164,0.0005507325,0.0002007037,0.0001403948,0.0007748214,0.001592812,0.007074689],"genre_scores_gemma":[0.9152303,0.0001038796,0.08168387,0.00008814526,0.00002647744,0.000180002,0.0005188845,0.00007567911,0.002092797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01896327,"threshold_uncertainty_score":0.03770578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.11926900025208,"score_gpt":0.2795721767172589,"score_spread":0.1603031764651789,"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."}}