{"id":"W6950629448","doi":"10.5524/100575","title":"Supporting data for \"GenPipes: an open-source framework for distributed and scalable genomic analyses\"","year":2019,"lang":"en","type":"dataset","venue":"GigaDB","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canarie","keywords":"Workflow; Scalability; Flexibility (engineering); Software; Software deployment; Genomics; Server; Metagenomics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003610952,0.002887189,0.001889302,0.003745824,0.001380799,0.002718457,0.005763779,0.002600401,0.2348717],"category_scores_gemma":[0.01041266,0.001245749,0.002047775,0.006739743,0.001065077,0.002709344,0.004316643,0.003195967,0.1413121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001522117,"about_ca_system_score_gemma":0.004480538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0141666,"about_ca_topic_score_gemma":0.0243591,"domain_scores_codex":[0.9980708,0.0003048959,0.0002583988,0.0005504917,0.0004765226,0.0003389415],"domain_scores_gemma":[0.9966666,0.001133314,0.000375428,0.0007072244,0.0005528147,0.000564608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001648093,0.00003416512,0.0006816558,0.00147216,0.00007384284,0.00007222457,0.00005181426,0.0005187482,0.0005739547,0.001263257,0.9922277,0.002865566],"study_design_scores_gemma":[0.0009414448,0.00003237944,0.002981802,0.0005443362,0.00005598263,0.0001514957,0.00009247172,0.001101683,0.001502386,0.006249457,0.9862698,0.00007676988],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001039686,0.00004026125,0.0004790649,0.00007640987,0.00003888817,0.00003061869,0.9967951,0.001855452,0.0005801655],"genre_scores_gemma":[0.0004175502,0.0000518959,0.0016547,0.0001184497,0.000007640861,0.0002026514,0.9965644,0.0006081644,0.0003746001],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2348717,"threshold_uncertainty_score":0.785724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1265652084631406,"score_gpt":0.4477554824733593,"score_spread":0.3211902740102188,"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."}}