{"id":"W4403496106","doi":"10.1051/bioconf/202412925037","title":"Towards 3D quantitative imaging in FIB-SEM for applications in battery materials","year":2024,"lang":"en","type":"article","venue":"BIO Web of Conferences","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Materials science; Battery (electricity); Nanotechnology; Computer science; Systems engineering; Engineering; Physics","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":[0.0004716477,0.0001145724,0.0002335323,0.0002290289,0.00002385851,0.0001201605,0.0002206667,0.00004386519,0.0003430111],"category_scores_gemma":[0.00002999359,0.00009493059,0.00002992538,0.0001904329,0.0001118674,0.0001625572,0.00003113332,0.00004780337,0.00001183078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003625475,"about_ca_system_score_gemma":0.000509423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000250377,"about_ca_topic_score_gemma":0.0003134645,"domain_scores_codex":[0.9990283,0.00004885613,0.000327048,0.0002659976,0.0001078238,0.0002219901],"domain_scores_gemma":[0.9996349,0.0001112255,0.00006449328,0.0001204773,0.00004782815,0.000021092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001844161,0.00002883234,0.002551221,0.0000941281,0.000001660854,9.639674e-7,0.0001841966,3.860122e-7,0.8685287,0.1270811,0.0001968797,0.001313491],"study_design_scores_gemma":[0.00009516367,0.00009240017,0.003872427,0.0001847666,0.000005817276,6.561116e-7,0.0002065356,0.0002505281,0.9750451,0.01537263,0.004751615,0.0001223886],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795591,0.001419612,0.01200339,0.0009947483,0.0002359114,0.0007607151,0.000134389,0.0001683899,0.004723728],"genre_scores_gemma":[0.9931117,0.0001141134,0.006319173,0.00003594456,0.00003039004,0.0003151672,0.00001799938,0.000008902629,0.0000466679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1117084,"threshold_uncertainty_score":0.3871157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02806339809597856,"score_gpt":0.3403367197548867,"score_spread":0.3122733216589081,"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."}}