{"id":"W4403780166","doi":"10.48550/arxiv.2409.14654","title":"Fast and Small Subsampled R-indexes","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Agencia Nacional de Investigación y Desarrollo","keywords":"Computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001924491,0.0002952511,0.0002730135,0.0002180427,0.0001031738,0.0003528486,0.001202081,0.0002737076,0.00001101965],"category_scores_gemma":[0.00001450563,0.0003216712,0.0001215972,0.0003564354,0.0001011066,0.0001217751,0.003379378,0.0007490263,0.0001105203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006845816,"about_ca_system_score_gemma":0.0001917099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002281217,"about_ca_topic_score_gemma":0.00009411043,"domain_scores_codex":[0.998209,0.00007314099,0.0001513483,0.001190322,0.00006241793,0.0003137433],"domain_scores_gemma":[0.9987232,0.00007206396,0.00008584198,0.0008386135,0.0000913679,0.0001888924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001172317,0.00004283954,0.0009739064,0.000222588,0.0001041274,0.0004713814,0.0005229665,0.03214872,0.00005780423,0.956314,0.0002595844,0.008870306],"study_design_scores_gemma":[0.0001182691,0.00002620544,0.0002632621,0.0001330292,0.00004621248,0.000008730663,0.00002251537,0.6498786,0.0000643037,0.3489785,0.0001151473,0.0003452301],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.230001,0.0002703764,0.7664366,0.000189776,0.0004853582,0.0001128503,0.00001264133,0.0003965899,0.002094821],"genre_scores_gemma":[0.9935325,0.0002332337,0.004110872,0.00009510203,0.00006626744,7.43064e-7,0.00000568344,0.00001674159,0.001938898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7635315,"threshold_uncertainty_score":0.9999235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1013808841000094,"score_gpt":0.1873929952634598,"score_spread":0.08601211116345031,"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."}}