{"id":"W4399692922","doi":"10.1016/j.engappai.2024.108765","title":"Subspace graph networks for real-time granular flow simulation with applications to machine-terrain interactions","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"CM Labs Simulations (Canada); Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Terrain; Subspace topology; Graph; Flow (mathematics); Artificial intelligence; Real-time computing; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003624441,0.0007453408,0.000525573,0.0004978022,0.0004510734,0.0006695064,0.0008010307,0.0009225194,0.003439024],"category_scores_gemma":[0.001903847,0.0004227466,0.0007073477,0.000639693,0.0005783574,0.0007712671,0.0008444493,0.001191286,0.0004977123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009587919,"about_ca_system_score_gemma":0.0009270139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01579636,"about_ca_topic_score_gemma":0.01874483,"domain_scores_codex":[0.9998713,0.00004262911,0.000005697199,0.00002435314,0.00004140137,0.00001471056],"domain_scores_gemma":[0.9993023,0.0004525471,0.00004839531,0.0000591867,0.00008690827,0.00005070915],"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":[0.00001381593,0.00001003364,0.000260293,0.00001468061,0.000009635057,0.00001849683,0.00001420097,0.9901212,0.0003631554,0.002993671,0.000590911,0.005589955],"study_design_scores_gemma":[9.65623e-7,9.968558e-7,0.00001486666,5.956285e-7,3.058023e-7,9.408547e-7,0.000001285373,0.9987621,0.00004405502,0.001045132,0.0001280329,7.478976e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0469586,0.0003842677,0.9434506,0.0007136239,0.000108416,0.00006931442,0.0004968243,0.002942828,0.004875635],"genre_scores_gemma":[0.6791412,0.000475393,0.3134179,0.0002055161,0.00006084031,0.0003595895,0.001194499,0.0005943138,0.004550771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01579636,"threshold_uncertainty_score":0.03140885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0091147064824666,"score_gpt":0.2607550814347271,"score_spread":0.2516403749522605,"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."}}