{"id":"W4380551893","doi":"10.17863/cam.101660","title":"Developments and Further Applications of Ephemeral Data Derived Potentials","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"Engineering and Physical Sciences Research Council; University of Cambridge; Science and Technology Facilities Council; Dell EMC","keywords":"Computer science; Suite; Computational science; Ternary operation; Parameterized complexity; Algorithm","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"],"consensus_categories":[],"category_scores_codex":[0.0008212375,0.000236416,0.0003609483,0.0001640075,0.0001517673,0.00009574794,0.002013928,0.0001651194,0.0003321344],"category_scores_gemma":[0.0001101571,0.000257901,0.00003969842,0.0003004005,0.0003499909,0.0002276311,0.005151686,0.0001866178,0.0002712314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004368837,"about_ca_system_score_gemma":0.000177842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000543977,"about_ca_topic_score_gemma":0.00005733161,"domain_scores_codex":[0.9978752,0.0001871706,0.0003093769,0.001191134,0.0001418149,0.0002953236],"domain_scores_gemma":[0.9977996,0.00009907687,0.0004404929,0.001429419,0.0001202787,0.0001111678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001506494,0.0002772439,0.02520168,0.001177555,0.0001746631,0.0001221164,0.001037442,0.1470967,0.8128182,0.009826365,0.001042463,0.001074937],"study_design_scores_gemma":[0.005295186,0.0003352452,0.2418195,0.001839059,0.001613758,0.00005308007,0.001908689,0.3929315,0.1846623,0.1510259,0.01117978,0.007336106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.89904,0.00003615357,0.09922625,0.0000710177,0.0004443858,0.0004390463,0.0003060482,0.0001793794,0.0002577514],"genre_scores_gemma":[0.989786,0.000153061,0.008934054,0.00002531532,0.00005411045,0.00000372044,0.000135396,0.00002816525,0.0008801196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6281559,"threshold_uncertainty_score":0.9999873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1256669326444813,"score_gpt":0.23563224365627,"score_spread":0.1099653110117886,"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."}}