{"id":"W4396837394","doi":"10.2139/ssrn.4819907","title":"Qf-Lca Dataset: Quantum Field Lens Coding Algorithm for System State Simulation and Strong Predictions","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Coding (social sciences); Lens (geology); Computer science; Algorithm; Field (mathematics); Through-the-lens metering; Quantum; State (computer science); Physics; Optics; Mathematics; Statistics; Quantum mechanics","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001933638,0.0002917187,0.000300469,0.0002797358,0.0004914447,0.0007309312,0.0007413591,0.0001505928,6.941638e-7],"category_scores_gemma":[0.00003702091,0.0002657458,0.0001544446,0.0001461284,0.00002244219,0.00005172553,0.001451526,0.002862803,0.000005188406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007591965,"about_ca_system_score_gemma":0.000911066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000809498,"about_ca_topic_score_gemma":0.00004635552,"domain_scores_codex":[0.9970179,0.0001092433,0.0004887991,0.0006423108,0.0003515292,0.001390214],"domain_scores_gemma":[0.9987633,0.0002520411,0.0002984845,0.0004858103,0.00008960834,0.0001107689],"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":[0.00001289451,0.00004272645,0.00001071975,0.0004253609,0.0007197483,0.00001955355,0.0004128423,0.4990085,0.000003073794,0.2338393,0.001236348,0.2642689],"study_design_scores_gemma":[0.0002773846,0.0002451412,0.000006644409,0.0004057061,0.0001138559,0.0001313137,0.0003474023,0.9510735,0.000003985075,0.04411823,0.003033986,0.0002428394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00362455,0.002896948,0.9895133,0.00125343,0.001762714,0.0004594758,0.0001721019,0.0002421547,0.00007537402],"genre_scores_gemma":[0.9930653,0.0004363965,0.004412046,0.00007521389,0.001082588,0.00003366663,0.00008831641,0.00004502827,0.0007614666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9894407,"threshold_uncertainty_score":0.9999795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197577514176258,"score_gpt":0.2781577721655669,"score_spread":0.2584000207479411,"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."}}