{"id":"W2097046002","doi":"10.1186/2046-1682-4-10","title":"A Bayesian method for inferring quantitative information from FRET data","year":2011,"lang":"en","type":"article","venue":"BMC Biophysics","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Imperial College London; McGill University","keywords":"Förster resonance energy transfer; Computer science; Inference; Fluorophore; Biological system; Bayesian probability; Data mining; Probability distribution; Algorithm; Statistics; Artificial intelligence; Fluorescence; Physics; Mathematics; Biology","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":[0.008955031,0.001636405,0.001664023,0.003168114,0.001112758,0.002070522,0.003047358,0.002157308,0.003476126],"category_scores_gemma":[0.03296093,0.001268728,0.001727354,0.002216206,0.001858033,0.002672292,0.002462575,0.00354491,0.001209089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001729066,"about_ca_system_score_gemma":0.002875493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004455768,"about_ca_topic_score_gemma":0.004244815,"domain_scores_codex":[0.9958156,0.002089171,0.0002707914,0.0007128798,0.0009605957,0.0001509917],"domain_scores_gemma":[0.9787445,0.01748299,0.0008605565,0.0009210521,0.001740273,0.0002504711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002505677,0.0001365911,0.001899221,0.000344217,0.0002009048,0.000162113,0.0001921314,0.6636865,0.004361029,0.09072661,0.00304316,0.234997],"study_design_scores_gemma":[0.00002120441,0.00002001018,0.0001674307,0.00003638192,0.00001962925,0.00005343859,0.00001021024,0.9331411,0.00120756,0.06375733,0.001542898,0.00002277619],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009064196,0.0000686603,0.9985635,0.00005078573,0.000006696046,0.00002578666,0.0000590624,0.0001366275,0.0001825232],"genre_scores_gemma":[0.05536101,0.0003064278,0.9418299,0.0001488413,0.00008409432,0.0003718783,0.0006947383,0.0001851271,0.001017948],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008955031,"threshold_uncertainty_score":0.04735929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07824574833141712,"score_gpt":0.3595523479616427,"score_spread":0.2813065996302256,"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."}}