{"id":"W4409623466","doi":"10.52783/iej.9","title":"Leveraging Generative AI for Database Migration: A Comprehensive Approach for Heterogeneous Migrations","year":2025,"lang":"en","type":"article","venue":"Indian Engineering Journal","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Gilead Sciences (Canada)","funders":"","keywords":"Generative grammar; Computer science; Database; Data science; Artificial intelligence","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":[0.0004179809,0.0001122314,0.000161034,0.0002677325,0.001164702,0.0002621518,0.0001769573,0.00006717625,0.00002297674],"category_scores_gemma":[0.0002264448,0.000119708,0.0001728763,0.0002980061,0.0000535562,0.0001813242,0.000009634891,0.0001712378,0.000001057935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001858493,"about_ca_system_score_gemma":0.000385409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001990572,"about_ca_topic_score_gemma":0.0009060368,"domain_scores_codex":[0.9990891,0.00006131356,0.000258524,0.0001821629,0.0001464372,0.0002625009],"domain_scores_gemma":[0.9990859,0.0002545111,0.00006755162,0.000127673,0.000349991,0.0001143571],"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":[0.00003302974,0.0001561616,0.0004578456,0.0001809385,0.0004700998,0.000006343683,0.03024991,0.9446471,0.001691186,0.007277169,0.008521001,0.006309164],"study_design_scores_gemma":[0.00111402,0.00007289265,0.0002198577,0.0001252946,0.0002122088,0.00001507678,0.008250211,0.804961,0.001495784,0.0007904761,0.1822993,0.0004438757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02095337,0.0003960668,0.9748674,0.00294109,0.0002359637,0.0004497012,0.00005492213,0.0000410108,0.0000604521],"genre_scores_gemma":[0.9575554,0.000037543,0.04028866,0.0006468398,0.0007011837,0.0002048831,0.0001461104,0.00001235304,0.000407007],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9366021,"threshold_uncertainty_score":0.8958066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02824971140519701,"score_gpt":0.3067951400769948,"score_spread":0.2785454286717978,"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."}}