{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002352576,0.00156569,0.001342825,0.00169224,0.001076466,0.00257233,0.002425088,0.00113599,0.01553719],"category_scores_gemma":[0.003929421,0.001250349,0.001375689,0.001156424,0.0007849662,0.00161059,0.002038185,0.002355929,0.007216754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013319,"about_ca_system_score_gemma":0.001920254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002078442,"about_ca_topic_score_gemma":0.004507982,"domain_scores_codex":[0.9986086,0.00011396,0.0001402155,0.0004856001,0.0005394808,0.0001120528],"domain_scores_gemma":[0.9978665,0.0007022376,0.0001778767,0.0006355161,0.0005379335,0.00008009299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004422401,0.0003164806,0.006455916,0.001821885,0.0002577862,0.0005176641,0.001078393,0.0585155,0.3516406,0.02285389,0.06654112,0.4895584],"study_design_scores_gemma":[0.00004522048,0.0001879893,0.006912758,0.0001785631,0.00008580978,0.0005387223,0.0002058442,0.6232796,0.2374827,0.02564287,0.1052057,0.0002341532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01342313,0.0002324746,0.9374363,0.0002431844,0.0001782805,0.0004502845,0.001569506,0.04137871,0.005088064],"genre_scores_gemma":[0.04717704,0.0003131375,0.9373453,0.000219846,0.00004611449,0.0008785567,0.002998034,0.006523076,0.004498946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01553719,"threshold_uncertainty_score":0.0519771,"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."}}