{"id":"W4401634064","doi":"10.1109/trpms.2024.3442773","title":"Non-Negative Matrix Factorization Using Partial Prior Knowledge for Radiation Dosimetry","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Radiation and Plasma Medical Sciences","topic":"Radiation Effects and Dosimetry","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dosimetry; Matrix decomposition; Factorization; Matrix (chemical analysis); Medical physics; Mathematics; Computer science; Physics; Medicine; Nuclear medicine; Materials science; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001357794,0.0009397253,0.0006646325,0.0005405917,0.0004137624,0.0006764398,0.0006172483,0.0008555847,0.001666298],"category_scores_gemma":[0.003434716,0.0004584516,0.001044778,0.0006569501,0.0008320819,0.001398341,0.0008647685,0.0014202,0.0008868804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007188675,"about_ca_system_score_gemma":0.0009759524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004065086,"about_ca_topic_score_gemma":0.003701866,"domain_scores_codex":[0.9994092,0.000203729,0.00002199484,0.0000980032,0.0002301904,0.00003687435],"domain_scores_gemma":[0.9987301,0.0008223535,0.0001247517,0.0001059733,0.0001867986,0.0000299685],"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":[0.0001364855,0.00006806375,0.0003678498,0.0002515923,0.00005737317,0.00008861757,0.0001621601,0.7743869,0.02931625,0.02723281,0.001781003,0.1661509],"study_design_scores_gemma":[0.000005993177,0.00002013425,0.0001512116,0.0000127317,0.000005733348,0.0000305302,0.000009267502,0.9852105,0.00436516,0.008771538,0.001401209,0.00001606852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002508442,0.000121533,0.9967555,0.00006017952,0.00001066563,0.00001454003,0.00003049866,0.000129437,0.0003691048],"genre_scores_gemma":[0.1186065,0.0006482691,0.8780133,0.00009966399,0.00005669327,0.0001612645,0.0003549321,0.0001330671,0.001926338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004065086,"threshold_uncertainty_score":0.008082867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02785229504301815,"score_gpt":0.3096253667992884,"score_spread":0.2817730717562702,"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."}}