{"id":"W6992934553","doi":"","title":"NEXT-GENERATION SEQUENCING AND MOTIF GRAFTING APPLICATIONS IN SYNTHETIC ANTIBODY DISCOVERY","year":2017,"lang":"en","type":"dissertation","venue":"University Library - University of Saskatchewan (University of Saskatchewan)","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Western Economic Diversification Canada; Canadian Institutes of Health Research; University of Toronto; Canadian Light Source","keywords":"Antibody; Motif (music); Drug discovery; DNA sequencing; Peptide sequence; Sequence motif; Sequence (biology); DNA; Cell culture","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000175893,0.0004730564,0.0007169599,0.0006286271,0.0006525136,0.00006060074,0.001181714,0.0006960566,0.0001187505],"category_scores_gemma":[0.00001142091,0.0007323665,0.0003405361,0.0003377662,0.0005517171,0.0004305773,0.000463371,0.0003951102,0.000006256613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001369785,"about_ca_system_score_gemma":0.001068992,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01181499,"about_ca_topic_score_gemma":0.02432836,"domain_scores_codex":[0.9978746,0.0001586384,0.0002420835,0.0009815852,0.0003265958,0.0004165421],"domain_scores_gemma":[0.9979687,0.00004065724,0.0008100069,0.0008343849,0.0001476542,0.0001985675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","study_design_scores_codex":[0.001340602,0.0005893945,0.02441907,0.001885459,0.0011894,0.0003684812,0.1677922,0.0003097601,0.7899796,0.00006745151,0.0003041201,0.01175452],"study_design_scores_gemma":[0.00193862,0.0002336097,0.009761607,0.0004139986,0.0004722073,0.000006547307,0.9686579,0.0002213117,0.01332461,0.00007883248,0.003981498,0.0009093273],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944709,0.0004672587,0.001649287,0.00009879893,0.0001161844,0.0006682705,0.0004254705,0.00004476746,0.002059081],"genre_scores_gemma":[0.9398401,0.001290314,0.003531576,0.000006978847,0.00006878716,7.104273e-8,0.004557264,0.00004800754,0.05065694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8008657,"threshold_uncertainty_score":0.9995127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01315327136281006,"score_gpt":0.1983822725474136,"score_spread":0.1852290011846036,"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."}}