{"id":"W4287279063","doi":"10.48550/arxiv.2103.05844","title":"BIKED: A Dataset for Computational Bicycle Design with Machine Learning\\n Benchmarks","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario College of Art and Design","funders":"","keywords":"Interpretability; Variety (cybernetics); Computer science; Machine learning; Class (philosophy); Dimensionality reduction; Artificial intelligence; Representation (politics); Key (lock); Space (punctuation); Data mining","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":[0.001084391,0.004114954,0.001169812,0.003047652,0.0008295465,0.001564507,0.00532783,0.003042858,0.02215793],"category_scores_gemma":[0.004455921,0.0008415827,0.002891174,0.0035753,0.0006978821,0.0009362649,0.001702624,0.001863017,0.016258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001789097,"about_ca_system_score_gemma":0.001552062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02557575,"about_ca_topic_score_gemma":0.07308066,"domain_scores_codex":[0.9986927,0.0002855309,0.0001161989,0.0003151786,0.0004568087,0.0001335629],"domain_scores_gemma":[0.998596,0.0004618108,0.00008626662,0.0004079609,0.0003342251,0.0001136101],"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":[0.0007337727,0.0009464768,0.009564831,0.004093265,0.0003515781,0.0006759714,0.0001573438,0.1650936,0.003035366,0.006594206,0.6635008,0.1452528],"study_design_scores_gemma":[0.0005432067,0.0005727311,0.01262369,0.0006372747,0.0001392105,0.0005580028,0.000396333,0.3020348,0.008594303,0.009942421,0.6637782,0.0001797147],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06349462,0.005103929,0.05809015,0.001080423,0.0008334299,0.001050232,0.801012,0.02950099,0.03983418],"genre_scores_gemma":[0.0500433,0.0008427987,0.05754337,0.0002640302,0.00003856525,0.00114549,0.8812872,0.0009512733,0.007883891],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02557575,"threshold_uncertainty_score":0.07412571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0957819264067106,"score_gpt":0.2101135624800674,"score_spread":0.1143316360733568,"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."}}