{"id":"W4401006491","doi":"10.1093/mam/ozae044.036","title":"Improvement of Boron Dopant Quantification Accuracy in Atom Probe Tomography via High Electric Field Analysis","year":2024,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Advanced Materials Characterization Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Atom probe; Dopant; Boron; Materials science; Electric field; Atom (system on chip); Field (mathematics); Tomography; Optoelectronics; Analytical Chemistry (journal); Nanotechnology; Doping; Optics; Chemistry; Physics; Computer science; Nuclear physics; Chromatography","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":[0.0005436174,0.0004997034,0.0002859653,0.0004599243,0.0002642011,0.000633394,0.0005079018,0.0007194023,0.001026272],"category_scores_gemma":[0.001039812,0.0003284446,0.000188581,0.0003642993,0.0003427715,0.001091736,0.0005387171,0.0003569036,0.0003001266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004439413,"about_ca_system_score_gemma":0.0004420284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001218616,"about_ca_topic_score_gemma":0.002037418,"domain_scores_codex":[0.9997408,0.00004420425,0.000011676,0.00006888398,0.0001068188,0.00002767018],"domain_scores_gemma":[0.9994876,0.0001866936,0.00008095415,0.0000654835,0.0001636778,0.00001560497],"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":[0.0001498191,0.00001554382,0.001006297,0.00007764783,0.00001001062,0.00004689411,0.00004654489,0.001835505,0.978834,0.001012576,0.0001844668,0.01678073],"study_design_scores_gemma":[0.000009446099,0.00007056678,0.002194943,0.00001003891,0.00002174799,0.0002112292,0.00003524568,0.06994324,0.9253169,0.0004964347,0.001667962,0.00002231758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4373132,0.001258999,0.5547293,0.0004944075,0.0001066263,0.00005649683,0.0003177435,0.001166729,0.004556388],"genre_scores_gemma":[0.7986038,0.0004418332,0.1987128,0.0001245569,0.00001999167,0.00003203984,0.0001323759,0.0001323257,0.001800272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001218616,"threshold_uncertainty_score":0.003433228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00456883182624522,"score_gpt":0.2459176848026005,"score_spread":0.2413488529763552,"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."}}