{"id":"W4389978759","doi":"10.1021/acs.bioconjchem.3c00434","title":"Design Parameters for a Mass Cytometry Detectable HaloTag Ligand","year":2023,"lang":"ca","type":"article","venue":"Bioconjugate Chemistry","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; Institute of Cancer Research; Ontario Institute for Cancer Research; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Chemistry; Mass cytometry; Alexa Fluor; Flow cytometry; Biophysics; Ligand (biochemistry); DOTA; Polymer; Biochemistry; Molecular biology; Chelation; Fluorescence; Organic chemistry; Receptor","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003982705,0.0005195512,0.0003996659,0.00006879458,0.0003358537,0.0001251693,0.0004778974,0.0007433252,0.00001403729],"category_scores_gemma":[0.0002152821,0.0005653895,0.0003070085,0.000580769,0.0002624034,0.000008712859,0.0002057636,0.0002149491,0.00006463714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000769843,"about_ca_system_score_gemma":0.0001946667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006089343,"about_ca_topic_score_gemma":5.385182e-7,"domain_scores_codex":[0.9972942,0.000033309,0.0004930795,0.001098757,0.0001855125,0.0008951749],"domain_scores_gemma":[0.9981723,0.0001053524,0.0002645461,0.0009958267,0.0001996904,0.0002623227],"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":[0.0001814741,0.00005424951,0.00001701211,0.0003035694,0.0001249732,0.000009075065,0.00001404962,0.0001151444,0.9846944,0.00001451094,0.01228631,0.002185284],"study_design_scores_gemma":[0.0007618355,0.000206511,0.000006717024,0.00008695336,0.00009012557,0.00002308885,0.00009279465,0.001486549,0.9289483,0.001300962,0.06633227,0.0006638403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3061913,0.002519931,0.6827364,0.001589405,0.0004318069,0.003297848,0.001161249,0.0007899983,0.001282046],"genre_scores_gemma":[0.8889897,0.002082481,0.09711822,0.0003467382,0.0006216468,0.0007000061,0.001198544,0.0001980285,0.00874457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5856182,"threshold_uncertainty_score":0.9996797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03221930785183046,"score_gpt":0.2921452937789509,"score_spread":0.2599259859271204,"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."}}