{"id":"W2943203310","doi":"10.1007/978-3-319-69251-7_20","title":"Depth Measurement Using a Microscope","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Microscope; Resolution (logic); Optics; Microscopy; 4Pi microscope; Materials science; Depth of focus (tectonics); Focus (optics); High resolution; Electron microscope; Conventional transmission electron microscope; Computer science; Physics; Artificial intelligence; Geology; Scanning transmission electron microscopy; Remote sensing","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":[0.00006247718,0.0001980382,0.000168254,0.00006104661,0.0000326178,0.00004652429,0.0001381022,0.0001560718,0.0002377248],"category_scores_gemma":[0.000001187093,0.0001957483,0.00006173368,0.00001352308,0.00001609034,0.00003541083,0.00003300335,0.0001807511,0.0002518518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001613758,"about_ca_system_score_gemma":0.00005204645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005682858,"about_ca_topic_score_gemma":0.000007361795,"domain_scores_codex":[0.9993927,7.653268e-7,0.0001597815,0.0001562771,0.0001601349,0.0001303179],"domain_scores_gemma":[0.9995738,0.00000282004,0.00003065977,0.0002843618,0.00007856717,0.00002985296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009695025,0.00005431847,0.000009956043,0.003165743,0.0006126402,0.00001665022,0.0001397814,0.002401398,0.2940495,0.3088026,0.1973356,0.1934021],"study_design_scores_gemma":[0.00008626084,0.000009487361,0.000001443869,0.0004599642,0.00007324202,0.00001110845,0.000001392586,0.01092732,0.01914494,0.005842164,0.9628745,0.0005681621],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000002714939,0.0009670983,0.21385,0.00001128385,0.00007017615,0.0002015974,0.000005489454,0.0005960558,0.7842956],"genre_scores_gemma":[0.007735922,0.0003561981,0.2544236,0.0002038082,0.0002325387,0.00002730029,0.00002662089,0.0003730216,0.736621],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7655389,"threshold_uncertainty_score":0.7982385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05126986617514105,"score_gpt":0.2556819517838276,"score_spread":0.2044120856086865,"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."}}