{"id":"W4311003603","doi":"10.1088/2632-2153/aca9ca","title":"Boost invariant polynomials for efficient jet tagging","year":2022,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Invariant (physics); Computer science; Jet (fluid); Artificial intelligence; Representation (politics); Particle physics; Theoretical computer science; Machine learning; Physics; Quantum mechanics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004284908,0.00008240057,0.0001243311,0.0001069396,0.001587776,0.00003431406,0.0001999552,0.000008692216,0.00007602685],"category_scores_gemma":[0.00004242431,0.0000700247,0.00002142671,0.0005110849,0.0005887161,0.00003392607,0.00055183,0.0001765424,0.000005127054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000287242,"about_ca_system_score_gemma":0.00003011914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000800366,"about_ca_topic_score_gemma":2.094199e-7,"domain_scores_codex":[0.9991815,0.00001743314,0.00009822521,0.0002563265,0.0001427803,0.0003037377],"domain_scores_gemma":[0.9997442,0.00004600946,0.00004695384,0.00009065352,0.00003229038,0.00003990485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001617715,0.00009819193,0.04037885,0.000002381429,0.00001611153,6.781157e-7,0.0003187362,0.0005376195,0.1567146,0.7860156,0.00005517082,0.01584593],"study_design_scores_gemma":[0.001942291,0.001107006,0.0007250526,0.00001315174,0.00005227741,0.00001310751,0.006750159,0.1828771,0.5962102,0.1560722,0.05352356,0.0007139613],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919009,0.0001646405,0.001182933,0.003842722,0.00007171454,0.0001497039,0.00001388932,0.00007154165,0.00260193],"genre_scores_gemma":[0.9994977,4.890936e-7,0.0002365556,0.00005636181,0.00002766852,0.0001164113,0.000002980583,0.000005910752,0.00005589218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6299434,"threshold_uncertainty_score":0.999712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007407402249508059,"score_gpt":0.2549133672859641,"score_spread":0.247505965036456,"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."}}