{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001406914,0.004339528,0.001462504,0.00255715,0.0009432538,0.002181075,0.004378589,0.002464037,0.0568581],"category_scores_gemma":[0.005127605,0.001016278,0.00240833,0.003739141,0.0005767175,0.001360884,0.00184111,0.002586326,0.111465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00216372,"about_ca_system_score_gemma":0.002785206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02246177,"about_ca_topic_score_gemma":0.03927619,"domain_scores_codex":[0.9988068,0.0001912915,0.0001204354,0.0004182478,0.0003060543,0.0001572714],"domain_scores_gemma":[0.9981986,0.0004048103,0.000101904,0.0006482396,0.0005118899,0.0001345973],"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":[0.0001575131,0.00007234836,0.00072964,0.0003744279,0.00004721205,0.00003951386,0.000019942,0.001819096,0.0003376123,0.0004346486,0.9897483,0.006219694],"study_design_scores_gemma":[0.0007878582,0.0001244235,0.005870744,0.0001990621,0.0000806434,0.0002045777,0.0001482512,0.008914041,0.004348272,0.003944676,0.975283,0.0000944315],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00100503,0.0001033497,0.001024402,0.0001457688,0.00007466766,0.00007336641,0.9851633,0.01075766,0.001652489],"genre_scores_gemma":[0.000819757,0.00003523119,0.001524656,0.00004046768,0.000007046924,0.0001542144,0.9961332,0.0004319505,0.0008534414],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0568581,"threshold_uncertainty_score":0.1902093,"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."}}