{"id":"W2011685133","doi":"10.1016/j.nima.2005.03.081","title":"Identifying markers of pathology in SAXS data of malignant tissues of the brain","year":2005,"lang":"en","type":"article","venue":"Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Royal University Hospital Foundation; University of Saskatchewan","keywords":"Small-angle X-ray scattering; Malignancy; Pathology; Medicine; Brain tissue; Synchrotron radiation; Scattering; Biomedical engineering; Optics; Physics","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":[0.0003668219,0.0003787794,0.0003596094,0.00203128,0.0003191957,0.0006201693,0.0002603696,0.0006143355,0.001936153],"category_scores_gemma":[0.001020053,0.0003302532,0.0002497089,0.001428093,0.0003211281,0.0004766035,0.000281729,0.000340381,0.000460228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002506148,"about_ca_system_score_gemma":0.0002365317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001395039,"about_ca_topic_score_gemma":0.001935023,"domain_scores_codex":[0.9998767,0.00001957302,0.00001797149,0.00001883587,0.00004088805,0.00002596381],"domain_scores_gemma":[0.9991913,0.0002276903,0.0002191649,0.00008996087,0.0001920198,0.00007987075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001998611,0.0002044717,0.1929366,0.0004382121,0.0001665561,0.002031435,0.0005668429,0.0265722,0.7266472,0.00143413,0.003549217,0.04345452],"study_design_scores_gemma":[0.00004129884,0.0002790919,0.5026605,0.00004578298,0.0001466989,0.004323643,0.0007151978,0.2333496,0.2530457,0.001894058,0.003420117,0.00007835799],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901184,0.0002002787,0.006702035,0.0001367776,0.00001125571,0.00001101113,0.001602573,0.0002832002,0.0009345161],"genre_scores_gemma":[0.9924494,0.000173742,0.004559197,0.00002581035,0.00001230355,0.000009670209,0.002264496,0.00004459233,0.0004607887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00203128,"threshold_uncertainty_score":0.006477118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07468646095529648,"score_gpt":0.4280454671599913,"score_spread":0.3533590062046948,"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."}}