{"id":"W7084403289","doi":"10.5281/zenodo.17246632","title":"NeoModeling Framework: Leveraging Graph-Based Persistence for Large-Scale Model-Driven Engineering (replication package)","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Library Science and Administration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Set (abstract data type); Source code; Replication (statistics); Loader; Code (set theory); R package; Data set; Cloud computing; Running time","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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001167248,0.0002019856,0.000200843,0.0003594652,0.004063224,0.001310497,0.001673492,0.0002379988,0.0007815158],"category_scores_gemma":[0.001587126,0.0002364201,0.0001336764,0.0008185973,0.0001293454,0.0006470668,0.0003886456,0.0004170478,0.000121979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000188092,"about_ca_system_score_gemma":0.00007391092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003483263,"about_ca_topic_score_gemma":0.000003942444,"domain_scores_codex":[0.9976458,0.0001907312,0.0003109335,0.0007370543,0.0005558452,0.0005596358],"domain_scores_gemma":[0.9982534,0.0001111718,0.0001774505,0.0007754253,0.0004809829,0.0002015604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001931068,0.00007800251,6.953254e-7,0.0001505347,0.00001595505,0.000001384677,0.001357511,0.01139468,0.00009675828,0.004035647,0.9812349,0.001614626],"study_design_scores_gemma":[0.0001716473,0.00006424231,0.000002546663,0.0001481975,0.00002768897,0.00000102604,0.0008108091,0.08131046,0.00004601429,0.0003822316,0.9167995,0.0002355983],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001460839,0.00004287723,0.4156677,0.0027443,0.0002450994,0.001053494,0.5769617,0.0006282391,0.00251054],"genre_scores_gemma":[0.008156289,0.0001475366,0.01062594,0.0004997386,0.0003132914,7.239118e-7,0.9795504,0.0002923847,0.0004137446],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4050418,"threshold_uncertainty_score":0.9997262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05722832386182555,"score_gpt":0.2912640871915201,"score_spread":0.2340357633296946,"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."}}