{"id":"W6968909824","doi":"10.5281/zenodo.4046983","title":"PyClone-VI: Supporting datasets","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Data file; Window (computing); Feature (linguistics); Data analysis","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005051366,0.004455929,0.004755676,0.003806696,0.002781822,0.00528299,0.009643161,0.00331926,0.3616377],"category_scores_gemma":[0.01875864,0.003655523,0.003671483,0.005603781,0.0009786452,0.004069415,0.004333425,0.005800645,0.2853665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001999883,"about_ca_system_score_gemma":0.004977379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006352496,"about_ca_topic_score_gemma":0.008483971,"domain_scores_codex":[0.9973909,0.0005091631,0.0002967094,0.0007309178,0.0007087525,0.0003636294],"domain_scores_gemma":[0.9945214,0.002432084,0.0002976636,0.001410299,0.0008518256,0.0004867142],"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":[0.0004327334,0.0000976255,0.001551892,0.001300928,0.0001346934,0.00009432906,0.0001016286,0.00194093,0.001638466,0.001823138,0.9843888,0.006494807],"study_design_scores_gemma":[0.001592332,0.0001207663,0.003982016,0.0006450629,0.0002375164,0.0002665698,0.0001610828,0.01090318,0.01052289,0.0218339,0.9495001,0.0002344877],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007657479,0.0001424139,0.008176476,0.0001959014,0.0001295019,0.0001437242,0.9149835,0.07212253,0.003340245],"genre_scores_gemma":[0.00321893,0.0001414775,0.01398818,0.0003855604,0.00004480985,0.001045839,0.9357507,0.04264022,0.002784316],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3616377,"threshold_uncertainty_score":0.9105464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1990611691964408,"score_gpt":0.3638752748982708,"score_spread":0.16481410570183,"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."}}