{"id":"W4285451316","doi":"10.32920/ryerson.14662569","title":"High performance computing for linear acoustic wave simulation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Computation; Parallel computing; Implementation; Computational science; Acoustic wave equation; Acoustic wave; Acoustic model; SPMD; Reduction (mathematics); Computer engineering; Algorithm; Acoustics; Mathematics; Physics; Speech recognition","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0009686656,0.001089041,0.001020643,0.0006749604,0.001006803,0.002096058,0.001857489,0.001050915,0.01129165],"category_scores_gemma":[0.00448041,0.0005209786,0.0008255954,0.001967302,0.0008770099,0.001543524,0.002061395,0.00272007,0.004077224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071861,"about_ca_system_score_gemma":0.001830306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003415114,"about_ca_topic_score_gemma":0.002065361,"domain_scores_codex":[0.9986719,0.0003765984,0.00006575151,0.0001149528,0.0006664129,0.0001043689],"domain_scores_gemma":[0.998613,0.0005301289,0.00006072961,0.0002885548,0.0004215766,0.00008610691],"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":[0.0001609563,0.0001326293,0.0009727836,0.0006165038,0.00009807909,0.0002740759,0.0002190864,0.4680581,0.009299532,0.3372874,0.04052299,0.1423579],"study_design_scores_gemma":[0.00003185585,0.00001640149,0.0001208164,0.00002766523,0.000008610857,0.0000381197,0.00001565396,0.9111431,0.00210573,0.06034966,0.02612698,0.00001537647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003620907,0.001271132,0.9711581,0.0005594969,0.0003060744,0.000135616,0.0003548454,0.003514826,0.0190791],"genre_scores_gemma":[0.1286156,0.00230224,0.8474427,0.0002803652,0.0002814703,0.001204063,0.001561138,0.0020696,0.0162428],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01129165,"threshold_uncertainty_score":0.03777432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06250193355150653,"score_gpt":0.2947362267526822,"score_spread":0.2322342932011757,"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."}}