{"id":"W4392239459","doi":"10.1007/s11075-024-01784-1","title":"Differential equation software for the computation of error-controlled continuous approximate solutions","year":2024,"lang":"en","type":"article","venue":"Numerical Algorithms","topic":"Numerical methods for differential equations","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Solver; Computer science; Ode; Computation; Software; Collocation (remote sensing); Focus (optics); Ordinary differential equation; Mathematical optimization; Applied mathematics; Mathematics; Algorithm; Differential equation; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005359317,0.0002565942,0.0006632848,0.0001257054,0.0002956324,0.0001129478,0.0002514904,0.0001272839,0.0001210122],"category_scores_gemma":[0.002493644,0.0001675343,0.0004675153,0.0005740087,0.0001516976,0.0001318829,0.00007931223,0.0002445054,0.00001731142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007613908,"about_ca_system_score_gemma":0.00008699694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003879871,"about_ca_topic_score_gemma":0.000001536158,"domain_scores_codex":[0.9977212,0.0002346992,0.0007981269,0.0003674633,0.0004728963,0.0004056441],"domain_scores_gemma":[0.9932801,0.005791454,0.0002504329,0.0002736777,0.0003144014,0.00008999858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003718483,0.0008400038,0.0000263731,0.0005756325,0.000974061,0.000002604098,0.0009229906,0.0009903787,0.001860952,0.149512,0.0009972091,0.842926],"study_design_scores_gemma":[0.001284497,0.0001532115,0.00009039963,0.00006065262,0.0004542054,0.000003558301,0.00007273551,0.6566303,0.0002902931,0.3405439,0.0002521663,0.0001640714],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001116579,0.0002882309,0.9942521,0.0008934275,0.001216426,0.001718818,0.0001129468,0.0003570846,0.00004434168],"genre_scores_gemma":[0.3911398,0.00001080393,0.6071463,0.00003497374,0.0004006632,0.0008338274,0.0000755221,0.00007621309,0.0002819466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8427619,"threshold_uncertainty_score":0.683185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1288798306174209,"score_gpt":0.3810409199111415,"score_spread":0.2521610892937206,"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."}}