{"id":"W2944307723","doi":"10.1038/s41467-019-10079-2","title":"Building a global alliance of biofoundries","year":2019,"lang":"en","type":"article","venue":"Nature Communications","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":307,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council","keywords":"Alliance; Genetically engineered; Biotechnology; Business; Computer science; Data science; Biology; Political science; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005033572,0.00005402311,0.00006249949,0.000008127307,0.00002789295,0.000005973256,0.0004544064,0.0001284102,0.000005389233],"category_scores_gemma":[0.00004264748,0.00005553941,0.00004131631,0.00009417075,0.00004335503,0.000001535441,0.0002001094,0.0001140854,0.000005393334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006911484,"about_ca_system_score_gemma":0.00002287962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005031057,"about_ca_topic_score_gemma":0.00005745326,"domain_scores_codex":[0.9996793,0.00001405728,0.00008834254,0.00009318321,0.0000494024,0.00007572155],"domain_scores_gemma":[0.998953,0.00001120366,0.00003179586,0.0009244112,0.00006018767,0.00001936862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000129451,0.00005204415,0.03964186,0.00002686184,0.00005747354,9.181652e-8,0.00002685792,0.0003622092,0.9246033,0.03114966,0.001671621,0.002395103],"study_design_scores_gemma":[0.0004496089,0.0001147912,0.05737474,0.00004949768,0.00002436364,0.00001285958,0.00008353557,0.0005815731,0.2499106,0.000545519,0.6905754,0.0002776076],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9470178,0.03813155,0.00591524,0.0007325253,0.0002656844,0.0001960651,0.00004158219,0.00002097632,0.007678521],"genre_scores_gemma":[0.971588,0.0006237284,0.02754876,0.00007914911,0.0000223673,0.000004843843,0.00003203496,0.000005204535,0.00009592806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6889037,"threshold_uncertainty_score":0.2264832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008059828732439104,"score_gpt":0.3432551545147631,"score_spread":0.335195325782324,"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."}}