{"id":"W2968060442","doi":"10.26434/chemrxiv.7051895.v1","title":"Measuring Reaction Rate Constant in Individual Cells to Facilitate Accurate Analysis of Cell-Population Heterogeneity","year":2018,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Population; Reaction rate constant; Constant (computer programming); Substrate (aquarium); Cell; Histogram; Chemistry; Biological system; Reaction rate; Kinetic energy; Biophysics; Kinetics; Computer science; Biology; Physics; Biochemistry; Catalysis","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002972009,0.0009737274,0.001213196,0.001762673,0.0003873051,0.001399065,0.001675056,0.001363621,0.002270175],"category_scores_gemma":[0.005822145,0.0005518142,0.0006505725,0.001328744,0.0006721001,0.002434871,0.0009151772,0.002119398,0.001484498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001187679,"about_ca_system_score_gemma":0.0005933085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008562636,"about_ca_topic_score_gemma":0.000576938,"domain_scores_codex":[0.9975567,0.0003051291,0.0001996136,0.00110023,0.0006446532,0.0001935624],"domain_scores_gemma":[0.9942275,0.002484282,0.001006489,0.001119125,0.0009617465,0.0002007793],"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":[0.0001588338,0.00007819275,0.003791111,0.0002413519,0.00003789956,0.00008238065,0.0002031547,0.001709209,0.9695842,0.00298613,0.000289817,0.0208377],"study_design_scores_gemma":[0.00001590112,0.0001760247,0.005993401,0.00002058617,0.00006755747,0.0002551706,0.00008803135,0.0418845,0.942319,0.00368865,0.005419204,0.00007185114],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1364684,0.002675021,0.8551212,0.0003215139,0.0002157616,0.0002544876,0.0009943858,0.001573844,0.002375413],"genre_scores_gemma":[0.6926806,0.002721012,0.2968387,0.0003939958,0.0001509713,0.0009920218,0.001351787,0.0005520537,0.004318849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002972009,"threshold_uncertainty_score":0.01571769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07098569918413056,"score_gpt":0.3043875292144287,"score_spread":0.2334018300302982,"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."}}