{"id":"W4388224550","doi":"10.5194/egusphere-2023-2491-supplement","title":"Supplementary material to \"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":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland; University of Ottawa","funders":"","keywords":"Artificial neural network; Glacial period; Artificial intelligence; Geology; Post-glacial rebound; Computer science; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006874255,0.001467937,0.0009374151,0.0008574353,0.000385297,0.001246307,0.002430776,0.001315512,0.628154],"category_scores_gemma":[0.005335957,0.0007208082,0.0007371792,0.001484565,0.0002065518,0.001287984,0.001273834,0.001264841,0.1920953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006228066,"about_ca_system_score_gemma":0.0008851936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007025381,"about_ca_topic_score_gemma":0.009650906,"domain_scores_codex":[0.9997196,0.00005106856,0.00002285282,0.00006529925,0.0001062711,0.00003486075],"domain_scores_gemma":[0.9982887,0.0006874675,0.00008344003,0.0002750457,0.0005324834,0.000132893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000109531,0.00009910102,0.0004918498,0.0005317302,0.00006640777,0.00008822528,0.00002410551,0.0155684,0.001382696,0.009092361,0.9443833,0.02816227],"study_design_scores_gemma":[0.0006212036,0.0001053836,0.002975456,0.000186688,0.00004233799,0.0002910959,0.00004852515,0.2305301,0.005161526,0.03682386,0.7230937,0.0001199445],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004128192,0.0004727052,0.2146392,0.001546149,0.004396523,0.0002362352,0.7159255,0.03318051,0.02547504],"genre_scores_gemma":[0.04952148,0.000726767,0.1515521,0.0009715066,0.001492944,0.0009645256,0.7180914,0.02541065,0.05126861],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.628154,"threshold_uncertainty_score":0.5303931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09053580795355498,"score_gpt":0.2801178989110929,"score_spread":0.1895820909575379,"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."}}