{"id":"W4410493676","doi":"10.1093/jmicro/dfaf025","title":"Auto-thresholding for unbiased electron counting","year":2025,"lang":"en","type":"article","venue":"Microscopy","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"Research Ireland; Exploratory Research for Advanced Technology; Japan Society for the Promotion of Science; Fusion Oriented REsearch for disruptive Science and Technology; Advanced Materials and Bioengineering Research","keywords":"Thresholding; Detector; SIGNAL (programming language); Computer science; Noise (video); Frame rate; Electron; Detection theory; Optics; Artificial intelligence; Computer vision; Physics; Image (mathematics)","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":[0.0005415015,0.0002043554,0.0002687126,0.0001419016,0.0003419091,0.0002199932,0.0003634108,0.0001125151,0.0001342335],"category_scores_gemma":[0.00007362941,0.0002033955,0.00009208782,0.0002668393,0.00007955364,0.0001547785,0.0000557212,0.0001509744,0.00003963025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000215646,"about_ca_system_score_gemma":0.0001775815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006758381,"about_ca_topic_score_gemma":0.00003189878,"domain_scores_codex":[0.9983917,0.00003243115,0.0002965197,0.0004515459,0.0001256808,0.0007021567],"domain_scores_gemma":[0.9993532,0.00009846107,0.00009646365,0.0003188392,0.00008916518,0.00004387137],"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":[0.0000731305,0.0000390007,0.0002957153,0.00004237762,0.000006627738,7.040589e-7,0.00003797779,0.000002027286,0.9730934,0.01018181,0.01604025,0.000186993],"study_design_scores_gemma":[0.000376201,0.0001458138,0.00007906817,0.00007687979,0.00002061993,0.000001286404,0.00001441163,0.0001903791,0.9435031,0.0034343,0.05197267,0.0001852022],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9118109,0.0008042696,0.08022252,0.0007687294,0.0005022212,0.0007030223,0.00002320988,0.0008150533,0.00435007],"genre_scores_gemma":[0.9400145,0.00004725979,0.05366827,0.002277946,0.0001445469,0.0002232008,0.0000254998,0.00004578657,0.003552971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03593242,"threshold_uncertainty_score":0.8294228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01031808660188653,"score_gpt":0.3195977555974052,"score_spread":0.3092796689955187,"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."}}