{"id":"W7056104319","doi":"","title":"Evidence accumulation for identifying discriminatory signatures in biomedical spectra","year":2005,"lang":"en","type":"article","venue":"NPARC","topic":"Particle accelerators and beam dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Spectral line; Noise (video); Pattern recognition (psychology); Feature (linguistics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008454876,0.0009857129,0.001308485,0.009078304,0.001158264,0.004303707,0.001546147,0.002204776,0.005893742],"category_scores_gemma":[0.04029575,0.0005342318,0.001369153,0.002860142,0.00158769,0.003767996,0.002607876,0.001968898,0.001450313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000757764,"about_ca_system_score_gemma":0.002161621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001186754,"about_ca_topic_score_gemma":0.001275265,"domain_scores_codex":[0.9973854,0.000557513,0.0004136338,0.0006464573,0.0008084382,0.0001885932],"domain_scores_gemma":[0.969681,0.02008884,0.002310652,0.00226266,0.004470815,0.001185896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005651359,0.0007046593,0.1107215,0.001275322,0.000768853,0.001009895,0.0004290498,0.01375693,0.06347038,0.01856518,0.004722463,0.7789243],"study_design_scores_gemma":[0.0007001261,0.002718456,0.1476521,0.0008834578,0.002797726,0.003525476,0.001319838,0.5766399,0.1460787,0.1014201,0.0158893,0.0003748742],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.431684,0.003868615,0.5427366,0.002604695,0.0003345558,0.0007291262,0.004128993,0.002208797,0.0117046],"genre_scores_gemma":[0.7879737,0.0007671512,0.2059604,0.0001855141,0.0002309767,0.0002120012,0.001928883,0.0001439971,0.002597334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009078304,"threshold_uncertainty_score":0.04471421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07128887066005613,"score_gpt":0.3275685960841621,"score_spread":0.256279725424106,"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."}}