{"id":"W4414428208","doi":"10.1038/s42256-025-01086-8","title":"Error-controlled non-additive interaction discovery in machine learning models","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Robustness (evolution); Trustworthiness; Feature (linguistics); Deep learning; Class (philosophy); Feature engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006041274,0.0002946484,0.0005280481,0.0005517554,0.0001865502,0.0003365084,0.0009236916,0.0002401937,0.00004472082],"category_scores_gemma":[0.0003705079,0.0002400369,0.000220616,0.001361317,0.00004492313,0.001504891,0.0004347106,0.002003787,0.00001934229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001385591,"about_ca_system_score_gemma":0.00006886902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006695649,"about_ca_topic_score_gemma":0.0007494143,"domain_scores_codex":[0.9979356,0.0001497384,0.0005844343,0.0006555738,0.0002969416,0.0003776956],"domain_scores_gemma":[0.998722,0.0004372336,0.0002327568,0.0004283663,0.0001237169,0.00005592342],"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":[0.0007045931,0.0003017319,0.002723241,0.00005361394,0.000270263,0.00009582981,0.001795438,0.4342716,0.0005446416,0.1839334,0.0002718589,0.3750338],"study_design_scores_gemma":[0.0004149716,0.00006063804,0.0002739265,0.0001274643,0.00002014142,0.000007747773,0.0001395252,0.9872475,0.001615619,0.008641596,0.001228966,0.0002219092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004833373,0.001953994,0.9728516,0.0009026515,0.0006767547,0.0002944861,0.000009035978,0.0001118476,0.01836626],"genre_scores_gemma":[0.9925036,0.0001580784,0.003993805,0.0004346434,0.00005991642,0.00003096374,0.00002989481,0.00001210898,0.002777004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9876702,"threshold_uncertainty_score":0.9788421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01090979156543081,"score_gpt":0.2746511474357972,"score_spread":0.2637413558703663,"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."}}