{"id":"W3212819223","doi":"10.1088/1367-2630/ac3883","title":"As good as it gets: a scaling comparison of DNA computing, network biocomputing, and electronic computing approaches to an NP-complete problem","year":2021,"lang":"en","type":"article","venue":"New Journal of Physics","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Defense Advanced Research Projects Agency","keywords":"DNA computing; Massively parallel; Benchmark (surveying); Computer science; Unconventional computing; Supercomputer; Computation; Scaling; NP; Subset sum problem; Parallel computing; Theoretical computer science; Algorithm; Turing machine; Mathematics","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":[0.0006335537,0.0002823411,0.0006372849,0.000036422,0.0001924172,0.00008882892,0.0003681199,0.0001565681,0.000003778009],"category_scores_gemma":[0.00007978394,0.0002504273,0.0002067315,0.0002447367,0.00007634883,0.00001210521,0.0004188782,0.000399702,0.000002514937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000267314,"about_ca_system_score_gemma":0.0002960991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002244631,"about_ca_topic_score_gemma":0.00001350224,"domain_scores_codex":[0.9976836,0.0002291229,0.0008007116,0.0004402607,0.0002724139,0.0005738765],"domain_scores_gemma":[0.9984119,0.00008084848,0.0007503939,0.0002386534,0.000237929,0.0002803054],"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":[0.0003679292,0.0009439097,0.02107614,0.0001873762,0.0006908195,0.0000360135,0.001847691,0.1002765,0.5577667,0.007555597,0.001465294,0.3077861],"study_design_scores_gemma":[0.006899733,0.01818633,0.007786043,0.001877337,0.0005909178,0.001551358,0.003415628,0.05887517,0.8052145,0.02546805,0.06737046,0.002764521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9386454,0.001971311,0.05818783,0.0003201257,0.0001300707,0.0001482039,0.000001851352,0.00001247029,0.0005827593],"genre_scores_gemma":[0.9700221,0.00003621166,0.02738301,0.0005502564,0.001924548,2.691933e-7,0.00002680006,0.00002667059,0.00003016591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3050216,"threshold_uncertainty_score":0.9999948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0591441130082886,"score_gpt":0.2946060402501146,"score_spread":0.235461927241826,"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."}}