{"id":"W2485453997","doi":"10.1021/acs.analchem.6b01712","title":"Substrate Engineering Enabling Fluorescence Droplet Entrapment for IVC-FACS-Based Ultrahigh-Throughput Screening","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Enzyme Catalysis and Immobilization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Shanghai Jiao Tong University; Ministry of Science and Technology of the People's Republic of China; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Genome British Columbia; Genome Canada","keywords":"Chemistry; Substrate (aquarium); Fluorescence; Cell sorting; Enzyme; Directed evolution; High-throughput screening; Biophysics; Protein engineering; Compartmentalization (fire protection); In vitro; Biochemistry; Nanotechnology; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001614009,0.0001990279,0.0001759113,0.00001947659,0.00006435096,0.00003625398,0.0001866344,0.0001675921,0.00007347264],"category_scores_gemma":[0.0002718591,0.000158055,0.0001751871,0.0001047539,0.00006397016,0.000008725616,0.00003536752,0.00006178177,0.00000467356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002855502,"about_ca_system_score_gemma":0.00005747583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002414421,"about_ca_topic_score_gemma":7.923839e-7,"domain_scores_codex":[0.9987062,0.000008311621,0.0002820559,0.0004786887,0.0001546777,0.0003700592],"domain_scores_gemma":[0.9992778,0.00005272914,0.00006713135,0.0003457718,0.0001155504,0.0001409593],"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":[0.0000442579,0.00004187529,0.0009031818,0.00006327205,0.0000579664,0.000001728699,0.000003484404,0.0005514671,0.9972175,0.00003526894,0.000427484,0.0006524952],"study_design_scores_gemma":[0.0006817356,0.00004965198,0.0002261945,0.00006358889,0.00004904282,0.000002925565,0.0000134749,0.006525341,0.9802893,0.000007092479,0.0118422,0.0002494104],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7148853,0.0002401319,0.284081,0.0003160837,0.0000505546,0.0001623246,0.00005362774,0.00004001319,0.0001709577],"genre_scores_gemma":[0.9963406,0.00005952863,0.002178058,0.00009045029,0.0002843469,0.00003556612,0.0003424626,0.00003004984,0.0006389843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2819029,"threshold_uncertainty_score":0.6445296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01210109145068657,"score_gpt":0.240791388179253,"score_spread":0.2286902967285664,"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."}}