{"id":"W2013279735","doi":"10.1016/j.ymeth.2013.07.042","title":"Measuring ligand–receptor binding kinetics and dynamics using k-space image correlation spectroscopy","year":2013,"lang":"en","type":"article","venue":"Methods","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Kinetics; Receptor–ligand kinetics; Biophysics; Fluorescence correlation spectroscopy; Membrane; Biomolecule; Molecular dynamics; Kinetic energy; Chemistry; Diffusion; Biological system; Molecule; Receptor; Physics; Thermodynamics; Biology; Biochemistry; Computational chemistry","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":[0.0005467005,0.0004510033,0.0003885386,0.0003198519,0.0004266829,0.0008075314,0.0008725151,0.0005573836,0.001395298],"category_scores_gemma":[0.001264728,0.0003492984,0.0002096609,0.0006223515,0.0005153708,0.0009074649,0.0003426417,0.001230023,0.0006583999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009098359,"about_ca_system_score_gemma":0.0007117355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002814986,"about_ca_topic_score_gemma":0.004263161,"domain_scores_codex":[0.9996284,0.0000562248,0.00001664311,0.00009351066,0.0001357658,0.0000694467],"domain_scores_gemma":[0.9992388,0.0003597062,0.000142306,0.0001071881,0.00009177849,0.00006018642],"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":[0.0001299118,0.0001040449,0.0004288949,0.00008600673,0.00002424128,0.0000387574,0.0000492876,0.001682035,0.9871487,0.001723468,0.000412756,0.008171937],"study_design_scores_gemma":[0.0000241543,0.000102495,0.003490125,0.000007462841,0.00001875536,0.000131243,0.00003726798,0.1388132,0.8553178,0.000703061,0.001309366,0.00004503555],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7398231,0.0006754721,0.2522514,0.0002947662,0.00007117318,0.0001405647,0.0005320709,0.001488533,0.004722896],"genre_scores_gemma":[0.9040207,0.0009902477,0.09038305,0.0001912461,0.00001967978,0.0001848583,0.0004563196,0.0001994084,0.00355438],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002814986,"threshold_uncertainty_score":0.006601334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02018247300003914,"score_gpt":0.3395833189732066,"score_spread":0.3194008459731675,"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."}}