{"id":"W4385072458","doi":"10.1093/micmic/ozad067.215","title":"Automated SEM Acquisitions and Segmentation With AI","year":2023,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Object Research Systems (Canada)","funders":"","keywords":"Materials science; Segmentation; Artificial intelligence; Computer science; Computer vision","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":[0.0002195432,0.0001636803,0.0002249139,0.0002322475,0.000321454,0.0002136229,0.00008457461,0.00006219645,0.0001624303],"category_scores_gemma":[0.000004593222,0.0001346219,0.00003387828,0.0006210095,0.0001500448,0.0002021095,0.00005352358,0.00007886279,0.00006410151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000332792,"about_ca_system_score_gemma":0.00002804082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001423536,"about_ca_topic_score_gemma":0.0001393002,"domain_scores_codex":[0.9989747,0.00005207348,0.0001783056,0.0003753296,0.0001092612,0.0003102854],"domain_scores_gemma":[0.9995987,0.00002752451,0.00006746386,0.0001781372,0.00005083099,0.00007736914],"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":[0.00002629059,0.0000190717,0.001148324,0.00001958153,0.00003474803,0.000006550401,0.0003247518,0.000004907818,0.9942585,0.00009738542,0.003843011,0.0002169249],"study_design_scores_gemma":[0.0002482285,0.0001257824,0.001522178,0.00002491884,0.0001469228,0.00002123665,0.0002114062,0.0006777503,0.995833,0.0001802747,0.0008267456,0.0001814953],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938498,0.0003317955,0.003693536,0.0007164209,0.00002533535,0.0001540054,0.0000435946,0.001056355,0.0001292253],"genre_scores_gemma":[0.9857531,0.0004917265,0.01187016,0.000698217,0.00002523745,0.00004488346,0.0001112456,0.0000248773,0.0009805304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008176619,"threshold_uncertainty_score":0.5489721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006822111120053402,"score_gpt":0.2899238007111745,"score_spread":0.2831016895911211,"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."}}