{"id":"W2761442445","doi":"10.1016/j.ejmp.2017.09.062","title":"Abstract ID: 115 A Monte Carlo-based eigenspectrum decomposition technique for computed tomography","year":2017,"lang":"en","type":"article","venue":"Physica Medica","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Université de Montréal","funders":"","keywords":"Monte Carlo method; Spectral line; Physics; Computational physics; Algorithm; Bremsstrahlung; Principal component analysis; Imaging phantom; Calibration; Photon; Optics; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006642945,0.0004660955,0.0004663304,0.0007139369,0.0004632958,0.0008313749,0.0005753916,0.0006888569,0.005100394],"category_scores_gemma":[0.001642673,0.000393917,0.0008358573,0.000728133,0.0004613661,0.0006970027,0.0008081337,0.001031794,0.001912568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002727463,"about_ca_system_score_gemma":0.0007900628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001774891,"about_ca_topic_score_gemma":0.002425557,"domain_scores_codex":[0.9997233,0.00009467047,0.00001296142,0.00002631874,0.0001232223,0.0000195739],"domain_scores_gemma":[0.9994633,0.0002138127,0.00004625595,0.00008715993,0.000146373,0.00004316705],"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":[0.0003276521,0.000187075,0.001534563,0.0002736258,0.0001385235,0.0003357102,0.0001905178,0.4196188,0.1022001,0.1464592,0.006771461,0.3219628],"study_design_scores_gemma":[0.00000541819,0.00001348501,0.0001788284,0.00001327707,0.000009170892,0.0001049545,0.000007507182,0.9872813,0.003934424,0.006462265,0.001977481,0.00001194735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002392763,0.00007306934,0.996405,0.00005969698,0.00002299308,0.00001339824,0.0000239118,0.0002082789,0.0008009668],"genre_scores_gemma":[0.09342784,0.0003526918,0.9008631,0.0001180865,0.00005936514,0.00008649058,0.0001788875,0.0004448763,0.004468626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005100394,"threshold_uncertainty_score":0.01706254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02219986353069106,"score_gpt":0.3445543989719995,"score_spread":0.3223545354413084,"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."}}