{"id":"W4404349436","doi":"10.1109/mmsp61759.2024.10743660","title":"Synthetic Local Data Augmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science","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":[0.001108241,0.001221541,0.0006989358,0.0007227551,0.0003321515,0.0009295567,0.001494275,0.001097102,0.00346964],"category_scores_gemma":[0.00480659,0.0004103685,0.001341286,0.0009048022,0.0008417448,0.001144497,0.001851833,0.00149541,0.001514628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00067884,"about_ca_system_score_gemma":0.000729422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004067695,"about_ca_topic_score_gemma":0.005089452,"domain_scores_codex":[0.9991224,0.0002120937,0.00005236546,0.0003250849,0.0002127843,0.00007512024],"domain_scores_gemma":[0.9976429,0.0007766105,0.0001507668,0.0008212057,0.00052642,0.0000819943],"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":[0.0009312597,0.000453062,0.008712985,0.0005928495,0.0001849286,0.0003739703,0.0003287984,0.6952102,0.0423998,0.004251737,0.01514762,0.2314127],"study_design_scores_gemma":[0.00002097343,0.0001371957,0.001978077,0.00002744694,0.00002067046,0.0001412262,0.00008292686,0.9660063,0.0199655,0.002476003,0.009113899,0.00002981068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2715943,0.00125821,0.6956852,0.0009344243,0.0007413885,0.0004097633,0.01008111,0.01174065,0.007554886],"genre_scores_gemma":[0.6985956,0.0003426162,0.2761614,0.0004476079,0.00009856971,0.000419298,0.01943807,0.0005977361,0.003899168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004067695,"threshold_uncertainty_score":0.01160711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04386647999921246,"score_gpt":0.3473052221115384,"score_spread":0.3034387421123259,"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."}}