{"id":"W4404569055","doi":"10.48550/arxiv.2411.09731","title":"To bootstrap or to rollout? An optimal and adaptive interpolation","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Interpolation (computer graphics); Computer science; Econometrics; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005512754,0.0002267271,0.0002776732,0.0006834605,0.0001270422,0.0003470505,0.0008016048,0.0002016098,0.0003711004],"category_scores_gemma":[0.0001580063,0.0002004187,0.0001076166,0.001132963,0.00005628308,0.0001894599,0.001577032,0.0003232299,0.000421928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001010437,"about_ca_system_score_gemma":0.000117637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001041951,"about_ca_topic_score_gemma":0.0001420817,"domain_scores_codex":[0.9979395,0.00009024044,0.0003182498,0.001262316,0.0001964436,0.0001932383],"domain_scores_gemma":[0.9981157,0.0002584152,0.0001239945,0.0008593075,0.0002918282,0.0003507891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001307801,0.0001993158,0.002460136,0.00002732122,0.000113613,0.0001363338,0.004222672,0.7319915,0.0004164313,0.2097428,0.01084484,0.0385372],"study_design_scores_gemma":[0.0002056119,0.0004121852,0.003514432,0.0001055508,0.00008343675,0.00000381643,0.002082017,0.7971059,0.0002294591,0.1856728,0.01001706,0.000567704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6172373,0.00001157615,0.3784073,0.0004050066,0.0001447536,0.0006530944,0.00009248196,0.0001934356,0.002854967],"genre_scores_gemma":[0.9870245,0.000007697749,0.006272728,0.000236242,0.00007812514,0.000006330084,0.000009587886,0.00001946255,0.006345334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3721346,"threshold_uncertainty_score":0.8172838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3596239622221302,"score_gpt":0.346796632024632,"score_spread":0.01282733019749821,"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."}}