{"id":"W4393713768","doi":"10.5281/zenodo.6345283","title":"Artifacts for the paper \"Concretization of Abstract Traffic Scene Specifications Using Multi-objective Optimization\"","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Computer graphics (images)","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":[0.001261466,0.002953864,0.001150373,0.002274889,0.0005237996,0.001874636,0.002825473,0.001588022,0.07934003],"category_scores_gemma":[0.005462564,0.000833828,0.002241719,0.003072304,0.0003882302,0.001072137,0.001609641,0.001747568,0.08414268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001479434,"about_ca_system_score_gemma":0.00170618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01313917,"about_ca_topic_score_gemma":0.0273417,"domain_scores_codex":[0.998598,0.0002749851,0.0001606112,0.0003901616,0.0004254851,0.0001507307],"domain_scores_gemma":[0.9975516,0.0006242044,0.0001428718,0.001027403,0.0005487119,0.0001052905],"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":[0.0001228146,0.00007163952,0.0009388524,0.001075671,0.00006388459,0.00004797597,0.00001942479,0.004291805,0.0005315485,0.001009496,0.9826024,0.009224542],"study_design_scores_gemma":[0.0005442424,0.00007135193,0.006258978,0.0004623213,0.00005799764,0.0001806698,0.0001002222,0.01634865,0.00392705,0.005318515,0.9666466,0.00008347377],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005504602,0.0000772112,0.002273731,0.00008778547,0.00009873054,0.0000669948,0.9876634,0.007176162,0.002005515],"genre_scores_gemma":[0.001228548,0.0000386799,0.002801775,0.00004756615,0.000008227991,0.0001588659,0.9946045,0.000445732,0.0006660604],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07934003,"threshold_uncertainty_score":0.2654188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2655625076595865,"score_gpt":0.3823398697153143,"score_spread":0.1167773620557278,"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."}}