{"id":"W4394051677","doi":"10.5281/zenodo.3560149","title":"AmpliconTagger pipeline databases","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Database; Pipeline (software); Computer science; Programming language","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":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006046858,0.000301656,0.0003055536,0.0003450481,0.001498055,0.001803741,0.005327248,0.0001261401,0.0103536],"category_scores_gemma":[0.0003862953,0.000288094,0.00008077126,0.0005334141,0.00009131301,0.0008228624,0.009257984,0.0005902861,0.04182652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001101539,"about_ca_system_score_gemma":0.00001076599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008206018,"about_ca_topic_score_gemma":5.187147e-7,"domain_scores_codex":[0.997182,0.0003107703,0.0003683993,0.0009788234,0.0006712036,0.0004888673],"domain_scores_gemma":[0.9963142,0.00005005753,0.0002440934,0.002695577,0.0004679846,0.0002281452],"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":[0.00001809715,0.0001138795,3.454119e-8,0.00007725877,0.00001916101,0.00002434581,0.00002727983,0.00001142452,0.00005459236,0.0006415694,0.962709,0.03630336],"study_design_scores_gemma":[0.000458415,0.0001075419,0.000008628219,0.00008711456,0.00001507897,0.0001174574,0.00001110527,0.001541046,0.00002820994,0.00003999175,0.9972458,0.0003396201],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002788011,0.0001449249,0.1290086,0.0002605372,0.0004555323,0.0004085427,0.8662294,0.000529167,0.002960505],"genre_scores_gemma":[0.00002588965,0.0003118112,0.001871987,0.0003726639,0.0003457955,4.207182e-8,0.9957319,0.0006349428,0.0007049279],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1295025,"threshold_uncertainty_score":0.9999571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06094851598872665,"score_gpt":0.2817164374006365,"score_spread":0.2207679214119098,"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."}}