{"id":"W2949343044","doi":"10.48550/arxiv.0711.1384","title":"On weighted approximations in $D[0, 1]$ with applications to self-normalized partial sum processes","year":2007,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Approximations of π; Mathematics; Applied mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001203135,0.0004574338,0.0006570235,0.0007587385,0.000272321,0.0002338701,0.00134998,0.0002727851,0.00009852279],"category_scores_gemma":[0.00331499,0.0003554745,0.00006724829,0.002648672,0.0001306815,0.0003080074,0.0005675835,0.0009180625,0.0007835813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002345126,"about_ca_system_score_gemma":0.0005016785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004015832,"about_ca_topic_score_gemma":0.0001800559,"domain_scores_codex":[0.9948757,0.0001227309,0.001216088,0.001481615,0.0016565,0.0006474228],"domain_scores_gemma":[0.9942292,0.002622973,0.0005099261,0.001253075,0.001025114,0.0003597209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001694857,0.003779158,0.8170065,0.001421666,0.0002549257,0.0002012849,0.007094151,0.07286771,0.000366459,0.04891277,0.001780649,0.04461989],"study_design_scores_gemma":[0.005891947,0.001122995,0.281409,0.002305178,0.000358743,0.00003333197,0.0029725,0.01418775,0.02214512,0.5606477,0.1027623,0.006163464],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2761968,0.00008919885,0.7173941,0.0004742526,0.0002441541,0.001656925,0.00008149258,0.0002674633,0.003595638],"genre_scores_gemma":[0.9231016,0.00001974768,0.0741979,0.0002237149,0.00028276,0.001674349,0.00004764502,0.00005493537,0.0003973681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6469048,"threshold_uncertainty_score":0.9999944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1297844714797771,"score_gpt":0.4242064193422787,"score_spread":0.2944219478625016,"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."}}