{"id":"W7081946321","doi":"10.1016/j.eswa.2025.129687","title":"Era splitting - Invariant learning for decision trees","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Decision tree; Disjoint sets; Boosting (machine learning); Generalization; Invariant (physics); Gradient boosting; Synthetic data; Decision theory","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":[0.0002823652,0.0001096003,0.0001483202,0.00006190218,0.0004641504,0.0001590381,0.0005122274,0.00006157787,0.000003044974],"category_scores_gemma":[0.0001287373,0.00008722815,0.00003567074,0.0004049686,0.00002347383,0.0001140571,0.000105712,0.00009976972,0.00001156995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003111855,"about_ca_system_score_gemma":0.0000779152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005181762,"about_ca_topic_score_gemma":0.000007417317,"domain_scores_codex":[0.9990309,0.00002601013,0.0002355269,0.0003964032,0.0001093176,0.0002017772],"domain_scores_gemma":[0.9987292,0.0004546795,0.0001016082,0.0005076405,0.0001580571,0.00004886951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002389446,0.0001169663,0.002213602,0.0001655552,0.00007091596,0.00000273135,0.001349234,0.01821837,0.008550576,0.9067985,0.009441692,0.053048],"study_design_scores_gemma":[0.0003444706,0.00003497759,0.000532213,0.0002547091,0.000005385629,0.00001790976,0.0005455532,0.1503203,0.001196109,0.003281126,0.8432726,0.0001946056],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002302549,0.0004830027,0.9821552,0.00214824,0.00008276073,0.0006984454,7.469219e-7,0.0001974089,0.01400396],"genre_scores_gemma":[0.8951426,0.000008704279,0.09477634,0.0001605907,0.0001287994,0.003329135,0.000009266081,0.000003715984,0.006440871],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9035173,"threshold_uncertainty_score":0.3569917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01230194023453397,"score_gpt":0.2590900639252136,"score_spread":0.2467881236906796,"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."}}