{"id":"W4403351515","doi":"10.1002/mp.17445","title":"Prediction of electron‐solid interaction parameters using machine learning","year":2024,"lang":"en","type":"article","venue":"Medical Physics","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Random forest; Mean squared error; Mean absolute percentage error; Computer science; Machine learning; Ensemble learning; Support vector machine; Artificial intelligence; Data mining; Statistics; Mathematics; Artificial neural network","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":[0.001055348,0.0001357125,0.0002182648,0.00006043104,0.00009992554,0.0001014834,0.0002525816,0.00007742687,0.000666314],"category_scores_gemma":[0.0005385721,0.0001138205,0.00006641539,0.0002925904,0.0001892328,0.000335888,0.0001001717,0.0004130564,0.0001174912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008448408,"about_ca_system_score_gemma":0.0001233331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002757354,"about_ca_topic_score_gemma":0.000004202284,"domain_scores_codex":[0.9980728,0.0001919835,0.0003540276,0.0003335947,0.000767212,0.000280373],"domain_scores_gemma":[0.9993678,0.0002032102,0.0001165419,0.0001626573,0.00004393487,0.0001058194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002262896,0.00004927013,0.0008032318,0.0001597614,0.00001094112,0.0000116916,0.0003356344,0.02044591,0.9746262,0.0003612366,0.00007768413,0.003095762],"study_design_scores_gemma":[0.00007917805,0.0001144863,0.00007983521,0.0001972869,0.00002018404,0.00002450816,0.00001035068,0.4737742,0.5241778,0.0009484509,0.0004905072,0.00008321891],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9009652,0.000113137,0.09642094,0.0001297256,0.001790295,0.0000815007,0.00001192976,0.0002349282,0.000252334],"genre_scores_gemma":[0.9976854,0.00001789458,0.001792108,0.00005251603,0.0003463834,0.000005360755,0.00001633395,0.00002205732,0.00006190479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4533283,"threshold_uncertainty_score":0.7295671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02763282625732414,"score_gpt":0.3137440704176535,"score_spread":0.2861112441603293,"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."}}