{"id":"W4400237029","doi":"10.11159/tann24","title":"Proceedings of the 8th International Conference on Theoretical and Applied Nanoscience and Nanotechnology (TANN 2024)","year":2024,"lang":"en","type":"paratext","venue":"Proceedings of the International Conference of Theoretical and Applied Nanoscience and Nanotechnology","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nanotechnology; Materials science; Engineering physics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00142607,0.0005879271,0.0009110124,0.0004826724,0.000328932,0.0004170282,0.00364746,0.0007674739,0.0007622592],"category_scores_gemma":[0.0008552612,0.0003602453,0.00009457355,0.0006556821,0.02307488,0.0002184729,0.004122247,0.0009829856,0.00001685021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005269588,"about_ca_system_score_gemma":0.0001901036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001441891,"about_ca_topic_score_gemma":0.0000015441,"domain_scores_codex":[0.9957124,0.00002019371,0.0009528309,0.001501412,0.001229818,0.0005833491],"domain_scores_gemma":[0.9977529,0.0002217534,0.001008305,0.000286465,0.0006033013,0.0001272935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001081369,0.00003659652,0.00004586246,0.0001595033,0.00001169659,1.707424e-7,0.0001322999,0.000001121299,0.4810471,0.5175643,0.0003062116,0.0005869911],"study_design_scores_gemma":[0.0003502691,0.000269463,0.00009793247,0.0007462341,0.00005949755,0.00008836557,0.0005499818,0.001377239,0.6943702,0.3009182,0.0008140773,0.0003585627],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9413065,0.0001509541,0.00006324242,0.01290504,0.001933439,0.0007397599,0.0002356091,0.00006202605,0.04260339],"genre_scores_gemma":[0.9959416,0.0009720337,0.001156292,0.000263297,0.00007121497,0.00007948166,0.00000216341,0.00002839224,0.001485504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2166461,"threshold_uncertainty_score":0.999885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01091821557449968,"score_gpt":0.2526012945776477,"score_spread":0.241683079003148,"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."}}