{"id":"W2901702185","doi":"10.5539/mas.v12n12p57","title":"3D Stereo Reconstruction of SEM Images","year":2018,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)","keywords":"Artificial intelligence; Pixel; Computer vision; Computer science; Magnification; Tilt (camera); Stereopsis; Stereo imaging; Matching (statistics); 3D reconstruction; Mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006283984,0.000095106,0.0001203079,0.0001562828,0.000152893,0.0001369809,0.001258316,0.0000332952,0.0000263672],"category_scores_gemma":[0.00003043869,0.00008009874,0.00002233309,0.0005620861,0.001069475,0.0006638754,0.0002840881,0.00007707829,0.00004373138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004101809,"about_ca_system_score_gemma":0.00008883674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005424857,"about_ca_topic_score_gemma":0.000001931736,"domain_scores_codex":[0.9986333,0.00001029353,0.0002017256,0.0004150689,0.0004692078,0.0002704251],"domain_scores_gemma":[0.9990612,0.00001616004,0.00008767936,0.0005116173,0.0002480971,0.00007527608],"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":[0.000003628894,0.00001657454,0.0001005061,0.000003235989,0.000001112059,1.032038e-7,0.0002232891,3.044161e-7,0.7879407,0.01857701,0.00004245126,0.1930911],"study_design_scores_gemma":[0.00007240608,0.0001337036,0.0007110831,0.00002105382,0.000001749885,0.000004554652,0.00001579414,0.04889812,0.9222401,0.02774019,0.00004544267,0.0001157756],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03579038,0.000007374043,0.8204151,0.00009028221,0.0001921272,0.0001201164,5.687417e-7,0.0001753154,0.1432087],"genre_scores_gemma":[0.8706433,0.000002100923,0.1291951,0.00006787819,0.00003491777,0.000008271473,6.503419e-8,0.000002869119,0.00004552913],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8348529,"threshold_uncertainty_score":0.3940527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02870448659389873,"score_gpt":0.2620372401832065,"score_spread":0.2333327535893078,"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."}}