{"id":"W3182017284","doi":"10.15587/1729-4061.2021.233944","title":"Integrating linear ordinary fourth-order differential equations in the MAPLE programming environment","year":2021,"lang":"en","type":"article","venue":"Eastern-European Journal of Enterprise Technologies","topic":"Numerical methods for differential equations","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ordinary differential equation; Maple; Mathematics; Power series; Eigenvalues and eigenvectors; Differential equation; Symbolic computation; Series (stratigraphy); Applied mathematics; Computer science; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00101866,0.0002468031,0.0003892436,0.0002555435,0.0001272831,0.0001447927,0.0008053852,0.00006096595,0.00007838852],"category_scores_gemma":[0.003804992,0.0001641689,0.0002276226,0.0004087642,0.0001539517,0.0002120497,0.0004209459,0.0008422063,0.00002667507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007052064,"about_ca_system_score_gemma":0.00003742469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002789227,"about_ca_topic_score_gemma":0.000007342647,"domain_scores_codex":[0.9969661,0.001015747,0.0009856811,0.0002328479,0.0004743963,0.0003252473],"domain_scores_gemma":[0.9979023,0.0007638131,0.00063124,0.0005274856,0.0001325896,0.00004258602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001225502,0.002182827,0.004492952,0.0001251529,0.0003158694,0.00178782,0.00611615,0.0002453873,0.01079962,0.006357717,0.0001875584,0.9672664],"study_design_scores_gemma":[0.01752152,0.009136463,0.01505076,0.008708972,0.002677887,0.004376424,0.2678198,0.04403792,0.03122452,0.5482643,0.04617715,0.005004236],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1988046,0.0002216907,0.7989113,0.001312027,0.0002051766,0.0001908276,0.000003203854,0.00009838357,0.0002527898],"genre_scores_gemma":[0.5948022,0.00003606474,0.404905,0.00002247967,0.00007562912,0.000009867328,0.000003135197,0.00003321299,0.0001124236],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9622622,"threshold_uncertainty_score":0.6694614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05878220549604542,"score_gpt":0.318038721592348,"score_spread":0.2592565160963026,"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."}}