{"id":"W2598322167","doi":"10.3389/fmicb.2017.00461","title":"Predictive Modeling of a Batch Filter Mating Process","year":2017,"lang":"en","type":"article","venue":"Frontiers in Microbiology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Foundation for Innovation","keywords":"Identifiability; Computer science; Biological system; Filter (signal processing); Extrapolation; Plasmid; Process (computing); Biochemical engineering; Mathematics; Machine learning; Biology; Statistics; Engineering; Gene; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001167087,0.00009918372,0.000168125,0.00004497787,0.00005959457,0.000008121251,0.0002775595,0.0001455592,0.000004414735],"category_scores_gemma":[0.00007996784,0.00009952444,0.00004369378,0.00001876316,0.00009562294,0.00000318847,0.0001011357,0.00008095991,6.562257e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007980231,"about_ca_system_score_gemma":0.00002615962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001381939,"about_ca_topic_score_gemma":0.00001211079,"domain_scores_codex":[0.9993611,0.00001830173,0.0001818692,0.0002264152,0.00001933414,0.0001929714],"domain_scores_gemma":[0.9995402,0.0000025012,0.00008041025,0.0003124389,0.00004293811,0.00002155931],"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":[0.00008536845,0.00002633669,0.04070541,0.0000614678,0.00005242835,0.000001878285,0.0002702301,0.02370062,0.9329597,0.000003542903,0.001008189,0.001124783],"study_design_scores_gemma":[0.001544292,0.0003300968,0.003340167,0.0001306505,0.00002999124,0.00003907507,0.0009331714,0.1079703,0.8834094,0.0004675649,0.00135505,0.0004502667],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8157268,0.0006347588,0.1825572,0.00003135538,0.0003887948,0.0001066784,0.0000242217,0.000004337944,0.0005258509],"genre_scores_gemma":[0.9905912,0.0000574459,0.009105145,0.00002247038,0.00007540084,0.00001271238,0.00004122388,0.00001259172,0.00008186654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1748643,"threshold_uncertainty_score":0.4058489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006569573616172563,"score_gpt":0.2776728609742807,"score_spread":0.2711032873581082,"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."}}