{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001326624,0.00005539571,0.00004405737,0.00005860457,0.00003002032,0.0001717921,0.001413445,0.00001796964,0.00008723402],"category_scores_gemma":[0.00001360573,0.00004268995,0.000009701668,0.000188967,0.00002491389,0.001600917,0.001170541,0.0000601086,0.0002694444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001986881,"about_ca_system_score_gemma":0.00002597614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009469103,"about_ca_topic_score_gemma":0.000001753514,"domain_scores_codex":[0.9992443,0.00001898509,0.00009680311,0.0003967178,0.0001489026,0.00009430943],"domain_scores_gemma":[0.998567,0.00007379855,0.000009792729,0.001305176,0.00001054813,0.00003364272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[4.086386e-7,0.000008006002,0.000001019525,0.000009107487,0.000002660077,0.0000148333,0.00002143457,0.00001252795,0.0008689137,0.2080526,0.04236016,0.7486483],"study_design_scores_gemma":[0.00002664381,0.00001524829,0.00001188548,0.00005008467,0.000002175768,0.00002037775,0.000009849829,0.7960207,0.02408906,0.02785255,0.1518074,0.0000939378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001399303,0.000238143,0.9935955,0.0008446213,0.0002313053,0.00006265528,0.00000988942,0.001483761,0.003520156],"genre_scores_gemma":[0.2755795,0.00003643746,0.723148,0.0003098464,0.00003196664,0.00001122756,0.0000498546,0.000007873654,0.0008252278],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7960082,"threshold_uncertainty_score":0.3463252,"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."}}