{"id":"W6926406856","doi":"10.25345/c5rw8b","title":"MassIVE MSV000084171 - Comparison of enrichment performance between HUNTER and TAILS","year":2019,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Noise (video); Identification (biology); Ectotherm; Sequence (biology)","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"],"consensus_categories":[],"category_scores_codex":[0.0007766139,0.0003438268,0.0006630699,0.0004208148,0.0001037446,0.001024446,0.003904084,0.0001653725,0.0002040909],"category_scores_gemma":[0.0001549882,0.0003068784,0.00006782199,0.0003327095,0.0001376579,0.007265724,0.003855476,0.0005928957,0.0003291947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006778169,"about_ca_system_score_gemma":0.00008539199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008656851,"about_ca_topic_score_gemma":0.00004028993,"domain_scores_codex":[0.9968221,0.0002105557,0.0005692472,0.0008624306,0.001023403,0.0005122974],"domain_scores_gemma":[0.99617,0.0004930074,0.0006911225,0.002398569,0.00009818394,0.0001490808],"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":[0.00001092854,0.00007992942,0.01326239,0.0005946683,0.0001192567,0.000009537424,0.00004988735,0.000009083873,0.000004330372,0.0001951451,0.9816214,0.00404342],"study_design_scores_gemma":[0.0003059688,0.0003200981,0.01266144,0.0001557152,0.00006677713,0.000001060827,0.00004150746,0.0005918584,0.0001299126,0.00001651535,0.9853909,0.0003182654],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002790314,0.0008011603,0.004111373,0.0004292284,0.0005467094,0.001050232,0.9887753,0.00004532604,0.001450347],"genre_scores_gemma":[0.01597781,0.00206888,0.002594656,0.0001794774,0.0002379321,0.0000583653,0.9773741,0.00002444519,0.001484327],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01318749,"threshold_uncertainty_score":0.9999383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09483180343423023,"score_gpt":0.3781758827041742,"score_spread":0.283344079269944,"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."}}