{"id":"W2916547323","doi":"10.1002/jbio.201800485","title":"The squared distance approach to frequency domain time‐resolved fluorescence analysis","year":2019,"lang":"en","type":"article","venue":"Journal of Biophotonics","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation","keywords":"Frequency domain; Sensitivity (control systems); Raw data; Computer science; Process (computing); Data mining; Time domain; Domain (mathematical analysis); Pattern recognition (psychology); Biological system; Artificial intelligence; Statistics; Mathematics; Electronic engineering; Engineering; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005893536,0.0001939294,0.0003191721,0.0001121934,0.0001175185,0.0000655227,0.0007994516,0.0001427142,0.000009830595],"category_scores_gemma":[0.00009975017,0.0001360263,0.0003110563,0.0005731363,0.0001120972,0.00001142279,0.0001175055,0.000228046,0.00001788034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007438308,"about_ca_system_score_gemma":0.000129919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004738684,"about_ca_topic_score_gemma":0.000005202162,"domain_scores_codex":[0.9984762,0.00007678271,0.000504148,0.0002986651,0.0003086066,0.0003355617],"domain_scores_gemma":[0.9984778,0.00002675006,0.0003882213,0.0006819869,0.0002847116,0.0001405019],"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":[0.0001879049,0.00006383419,0.002228877,0.000007860659,0.0001814391,0.000003646182,0.0000737141,0.0001267081,0.995825,0.0001965167,0.0007358009,0.0003686915],"study_design_scores_gemma":[0.0007184995,0.001097943,0.002211434,0.00006145272,0.0002016732,0.00006768832,0.0003363901,0.0007878885,0.9440832,0.001332337,0.04854064,0.0005609134],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7526549,0.002555878,0.2419072,0.0002580881,0.0002493272,0.0006048225,0.00004097037,0.00002187164,0.001706974],"genre_scores_gemma":[0.5711234,0.0007123292,0.4271131,0.0002509846,0.000117662,0.00001592716,0.00002617068,0.00004107829,0.0005994305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1852058,"threshold_uncertainty_score":0.5546991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00351553658389681,"score_gpt":0.2368255974407186,"score_spread":0.2333100608568218,"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."}}