{"id":"W4403821479","doi":"10.48550/arxiv.2410.00838","title":"Better Boosting of Communication Oracles, or Not","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Boosting (machine learning); Computer science; 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":[0.0002422065,0.0001965102,0.0002864351,0.0001652208,0.00009667076,0.00004663007,0.001152282,0.0001369259,0.00001652279],"category_scores_gemma":[0.0000428351,0.0001874856,0.0001122286,0.0003984099,0.0001081414,0.0003341968,0.004965781,0.0004742763,0.00005930977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008482153,"about_ca_system_score_gemma":0.000156148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003148326,"about_ca_topic_score_gemma":0.0001154864,"domain_scores_codex":[0.9987721,0.0001120933,0.0002464099,0.0006096693,0.00007862769,0.0001810908],"domain_scores_gemma":[0.9976354,0.0001573169,0.0002822254,0.001734862,0.0001277559,0.00006239978],"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":[0.00005238158,0.0000470278,0.0003418498,0.0005785446,0.0001052029,0.0002577457,0.0005687842,0.01846505,0.000282229,0.9738849,0.0009845266,0.004431776],"study_design_scores_gemma":[0.001034994,0.000179023,0.001410883,0.004571237,0.0002781246,0.0000476626,0.0007187396,0.7923557,0.006604993,0.1304022,0.06038229,0.00201425],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.123706,0.0002958836,0.8725344,0.0002462269,0.0004591606,0.0002558885,0.0001107833,0.0002847798,0.002106831],"genre_scores_gemma":[0.968814,0.000142963,0.02932514,0.00008830712,0.00004199708,9.678016e-7,0.00003145092,0.00001530425,0.001539898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.845108,"threshold_uncertainty_score":0.7645441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1017879772693964,"score_gpt":0.2147237305778385,"score_spread":0.1129357533084421,"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."}}