{"id":"W6910556547","doi":"10.48448/vhhs-jq69","title":"Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks","year":2024,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Discriminator; Replicate; Generative grammar; Consistency (knowledge bases); Fidelity; Adversarial system; Weighting; Discriminative model; Resampling","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":[0.004679388,0.0008567633,0.00107476,0.001344954,0.0008821515,0.0007430569,0.001110309,0.0004584824,0.0007631937],"category_scores_gemma":[0.001282805,0.0008317074,0.000264289,0.001394469,0.002025827,0.0004864428,0.0005202011,0.00100237,0.0008026375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001359571,"about_ca_system_score_gemma":0.002261911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00122285,"about_ca_topic_score_gemma":0.005229678,"domain_scores_codex":[0.9933569,0.0002786012,0.001152931,0.002131404,0.001595403,0.001484766],"domain_scores_gemma":[0.9964782,0.0007560984,0.001095037,0.0006898138,0.0005499061,0.0004309366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002953432,0.0003188186,0.00009754419,0.001472313,0.0008129444,0.00007207607,0.005997649,0.07414985,0.08955944,0.06718888,0.7528164,0.007218692],"study_design_scores_gemma":[0.006191518,0.0006184573,0.00004522498,0.00652166,0.0007578523,0.00008392541,0.009690807,0.5817267,0.01221764,0.044124,0.3321396,0.005882647],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001096772,0.002526115,0.9539013,0.000475284,0.008460188,0.002653134,0.002239097,0.001488117,0.02716002],"genre_scores_gemma":[0.5773484,0.0000984236,0.1888817,0.002369649,0.03184187,0.0007176474,0.006711592,0.004556891,0.1874738],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7650195,"threshold_uncertainty_score":0.9999753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03938605025177438,"score_gpt":0.3444230031017866,"score_spread":0.3050369528500122,"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."}}