{"id":"W2904992418","doi":"10.1017/s1431927618015234","title":"Multi-Angle Plasma Focused Ion Beam (FIB) Curtaining Artifact Correction Using a Fourier-Based Linear Optimization Model","year":2018,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"University of Connecticut","keywords":"Artifact (error); Focused ion beam; Computer science; Discretization; Materials science; Segmentation; Sample (material); Image processing; Computer vision; Artificial intelligence; Optics; Algorithm; Image (mathematics); Ion; Mathematics; 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":[0.0003254052,0.0002080327,0.0002541722,0.0003262615,0.0005833675,0.0001229954,0.0001237301,0.0001240614,0.0003270398],"category_scores_gemma":[0.0000479856,0.0001934926,0.000111145,0.0004400206,0.0001891627,0.0002816631,0.00001216422,0.0001446984,0.00003207713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002078169,"about_ca_system_score_gemma":0.00007412751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003328257,"about_ca_topic_score_gemma":0.0001487848,"domain_scores_codex":[0.9987624,0.00006766631,0.0002875387,0.000423605,0.0001435393,0.0003152496],"domain_scores_gemma":[0.9993324,0.00004910143,0.0001664416,0.0002180616,0.0001248106,0.0001092322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001932293,0.00006802483,0.02607007,0.0000207164,0.00009550627,0.000003436123,0.0006830355,0.8629334,0.08233588,3.457615e-7,0.001943087,0.02565327],"study_design_scores_gemma":[0.0003538229,0.0000869075,0.00009782472,0.00003715663,0.0001323526,0.000005772149,0.0001191163,0.8154413,0.1830349,0.000006465533,0.0004886501,0.0001957347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4673547,0.000113023,0.5319409,0.00007071561,0.0001946558,0.00009667699,0.000029012,0.0001089317,0.00009135924],"genre_scores_gemma":[0.6736066,0.00002833806,0.3251181,0.0006682309,0.00008910497,0.000001050846,0.0001619476,0.00001025453,0.0003163267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2068228,"threshold_uncertainty_score":0.78904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02740593730814422,"score_gpt":0.2647025207131217,"score_spread":0.2372965834049775,"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."}}