{"id":"W4398221349","doi":"10.21203/rs.3.rs-4396774/v1","title":"Engineering an Artificial Catch Bond with DNA","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Bond; DNA; Computer science; Artificial intelligence; Business; Biology; Genetics; Finance","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":[0.0001451317,0.0003099783,0.0002124109,0.0001768018,0.0002857432,0.0004193868,0.0004775128,0.0007248427,0.003028607],"category_scores_gemma":[0.0004414065,0.0003219392,0.0002291097,0.000169546,0.0002824226,0.0004694159,0.0007030308,0.0005752394,0.001467917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004232767,"about_ca_system_score_gemma":0.0002404603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003621268,"about_ca_topic_score_gemma":0.000523576,"domain_scores_codex":[0.9998419,0.00001263964,0.00001133969,0.0000442202,0.00006037577,0.00002947516],"domain_scores_gemma":[0.9998141,0.00004379141,0.00005192975,0.00002287326,0.00002618453,0.00004108498],"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":[0.00007723847,0.00008444348,0.0003573825,0.0001992332,0.00002447601,0.0001767824,0.00007629873,0.004812432,0.9757512,0.007734891,0.0007064624,0.009999196],"study_design_scores_gemma":[0.00004712148,0.0003405782,0.000374922,0.0000215599,0.00001715545,0.0001223489,0.00003940434,0.02347658,0.9527041,0.00111782,0.02170338,0.00003496989],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9062783,0.001268312,0.07442857,0.0005052584,0.0005211054,0.0001604887,0.0002866505,0.0008053647,0.01574604],"genre_scores_gemma":[0.9489974,0.0006016024,0.03678763,0.0001588009,0.00002844728,0.0001495955,0.0001959785,0.0002139173,0.01286667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003028607,"threshold_uncertainty_score":0.01013166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03223381880959825,"score_gpt":0.3735534188314735,"score_spread":0.3413196000218752,"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."}}