{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003853174,0.0006577736,0.0003859396,0.0002643886,0.0001666289,0.0005957448,0.0004405424,0.0006109515,0.0005384501],"category_scores_gemma":[0.0004675477,0.0002596482,0.000266056,0.0002603932,0.000249966,0.0003910809,0.0003922408,0.0005022726,0.0005168166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004531909,"about_ca_system_score_gemma":0.000253725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003619372,"about_ca_topic_score_gemma":0.0005627461,"domain_scores_codex":[0.9995993,0.00008344797,0.00003557623,0.00008965562,0.0001478102,0.00004421638],"domain_scores_gemma":[0.9998111,0.00006024892,0.00004041736,0.00002611881,0.00004191441,0.00002015874],"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":[0.00001549368,0.000008955722,0.00007830247,0.00002299797,0.000002336013,0.00003344137,0.000007607861,0.0002407524,0.9972143,0.0001458855,0.0000310646,0.002198891],"study_design_scores_gemma":[0.00000243052,0.00002223306,0.0000948137,0.000001039446,0.000002224713,0.00005152755,0.00000424898,0.001839144,0.997261,0.00002730861,0.0006905209,0.000003409752],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6604353,0.001966772,0.3319277,0.0002827576,0.00009473008,0.0003722894,0.0007063461,0.00106279,0.003151414],"genre_scores_gemma":[0.7928376,0.001655984,0.2015605,0.0001007114,0.00001338297,0.0002067697,0.0007863552,0.0001121027,0.00272651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006577736,"threshold_uncertainty_score":0.00328815,"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."}}