{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003640498,0.0005944683,0.0005124027,0.00112469,0.0002150382,0.001003001,0.0007707187,0.0007617696,0.004390279],"category_scores_gemma":[0.0007488196,0.0005725998,0.001044774,0.0005481596,0.0003334781,0.0007176659,0.0008550655,0.0006674182,0.001756601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003564143,"about_ca_system_score_gemma":0.0007402138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001322103,"about_ca_topic_score_gemma":0.002069242,"domain_scores_codex":[0.9993283,0.00006198735,0.00002739995,0.0001148816,0.0004297134,0.00003776872],"domain_scores_gemma":[0.9995614,0.00008075617,0.00004345241,0.0001616682,0.0001373249,0.00001541367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001600302,0.0001000283,0.001878212,0.0004478749,0.0001293051,0.0004450064,0.0002959483,0.1310923,0.3264815,0.02104948,0.003968674,0.5139517],"study_design_scores_gemma":[0.00001705105,0.0001155752,0.002487479,0.00003358649,0.00002995197,0.001134977,0.00009510551,0.8657591,0.1061697,0.006920218,0.01716745,0.00006984674],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005188722,0.00005412834,0.9929254,0.00002120792,0.00001463223,0.00002298713,0.0001120845,0.0007037531,0.0009571717],"genre_scores_gemma":[0.1125953,0.0002700593,0.8836578,0.00004981303,0.00001814608,0.00007649417,0.0006070501,0.0002460433,0.002479421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004390279,"threshold_uncertainty_score":0.01468694,"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."}}