{"id":"W2898651384","doi":"10.1101/457051","title":"baRcodeR with PyTrackDat: Open-source labelling and tracking of biological samples for repeatable science","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Queen's University; Government of Nunavut","keywords":"Computer science; Python (programming language); Data curation; Pipeline (software); Biological data; Data science; Data mining; Inference; R package; Data collection; Sample (material); Information retrieval; Artificial intelligence; Bioinformatics; Programming language; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.01119449,0.00224731,0.002399249,0.003095416,0.001938184,0.004895117,0.00605229,0.00275441,0.02308529],"category_scores_gemma":[0.0342011,0.002702618,0.002806354,0.002605242,0.002685095,0.004147803,0.006246694,0.007282573,0.03617886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001342499,"about_ca_system_score_gemma":0.00564272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002785992,"about_ca_topic_score_gemma":0.003358986,"domain_scores_codex":[0.9909887,0.001589254,0.0009058813,0.002476203,0.003524264,0.0005158306],"domain_scores_gemma":[0.9792538,0.008075087,0.002636161,0.006174208,0.002802592,0.001058204],"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":[0.001988304,0.0003412098,0.01207733,0.006379389,0.0007979485,0.0009951396,0.002234172,0.01187903,0.1908686,0.0358561,0.4857101,0.2508726],"study_design_scores_gemma":[0.0002866507,0.0002598094,0.006014298,0.00108911,0.0001827586,0.0008311945,0.0001752697,0.05006803,0.2903105,0.03371827,0.616294,0.0007699964],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.00367885,0.0005130882,0.711604,0.0005702709,0.0007503125,0.0003463769,0.02255529,0.257337,0.002644751],"genre_scores_gemma":[0.02905506,0.0009174175,0.8203731,0.001752749,0.0002471891,0.003447142,0.04449601,0.09269227,0.007018996],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.9939477,"threshold_uncertainty_score":0.07722801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04245660854919944,"score_gpt":0.2753293393142184,"score_spread":0.2328727307650189,"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."}}