{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001399431,0.002814733,0.001625967,0.004673928,0.001357308,0.002778262,0.004181765,0.001731177,0.09270769],"category_scores_gemma":[0.006594968,0.001044395,0.001296842,0.005538457,0.0004979892,0.002482424,0.002700073,0.001853995,0.1735232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001473533,"about_ca_system_score_gemma":0.00213104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008815724,"about_ca_topic_score_gemma":0.01341276,"domain_scores_codex":[0.9982896,0.0001609219,0.0002764542,0.000567818,0.0004939106,0.0002112383],"domain_scores_gemma":[0.9969766,0.0006968545,0.0002223735,0.00103681,0.0008804477,0.0001869406],"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":[0.00013418,0.00003005236,0.0004617129,0.0005203277,0.00002506631,0.00003480779,0.00004579397,0.000220781,0.001054555,0.0009218469,0.9895566,0.006994244],"study_design_scores_gemma":[0.0001338139,0.0000358291,0.002012329,0.0001438235,0.0000400301,0.0001581877,0.00009212724,0.001324772,0.004923728,0.003372857,0.9877038,0.00005871544],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003665491,0.00008617258,0.001265304,0.0000763106,0.00004837653,0.00005007178,0.9841801,0.0117328,0.002194218],"genre_scores_gemma":[0.0005141968,0.0000379991,0.001420508,0.00004347608,0.000006479544,0.0001399331,0.9958962,0.001003383,0.0009377475],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09270769,"threshold_uncertainty_score":0.3101381,"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."}}