{"id":"W2621706803","doi":"10.5281/zenodo.802906","title":"Segway 2.0 Application Note Datasets","year":2017,"lang":"en","type":"dataset","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":"Princess Margaret Cancer Centre; University of British Columbia","funders":"","keywords":"Computer science; Information retrieval","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":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.008180475,0.0002836136,0.0003477703,0.0007616359,0.005864489,0.009378369,0.01138178,0.0001626898,0.01554666],"category_scores_gemma":[0.01017076,0.0002579299,0.0001104716,0.0008007805,0.0003391503,0.0005347795,0.0102411,0.0005144628,0.1615686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001538464,"about_ca_system_score_gemma":0.0000104109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001129161,"about_ca_topic_score_gemma":0.000005054083,"domain_scores_codex":[0.9941014,0.0006322004,0.0007109821,0.001762439,0.002284246,0.0005087364],"domain_scores_gemma":[0.9910477,0.000168407,0.0007894739,0.006854954,0.0008529428,0.0002865567],"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":[0.00001401667,0.00007286252,1.041872e-7,0.00002288486,0.00001569142,0.00001173358,0.00003427254,0.00002444557,0.00001644269,0.00009161085,0.8072181,0.1924779],"study_design_scores_gemma":[0.0002320878,0.00005909491,0.00007097208,0.00003169847,0.00002886748,0.00003607636,0.00004422358,0.0006944576,0.00000693828,0.0005010663,0.9980141,0.0002804008],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000009410628,0.00003690803,0.01078384,0.000647777,0.0007453119,0.0005492793,0.9738194,0.0003288343,0.01307929],"genre_scores_gemma":[0.0003453904,0.00004472918,0.0001793011,0.0001766851,0.0003771499,6.793486e-8,0.9966708,0.0004573545,0.001748542],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1921975,"threshold_uncertainty_score":0.9999873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1313714826336436,"score_gpt":0.3893093966844114,"score_spread":0.2579379140507677,"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."}}