{"id":"W2063418135","doi":"10.1016/j.ultramic.2006.07.002","title":"An algorithm for 3-D refractive index measurement in holographic confocal microscopy","year":2006,"lang":"en","type":"article","venue":"Ultramicroscopy","topic":"Digital Holography and Microscopy","field":"Physics and Astronomy","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Refractive index; Holography; Confocal microscopy; Optics; Digital holographic microscopy; Confocal; Distribution (mathematics); Microscopy; Phase (matter); Materials science; Physics; Mathematics; Mathematical analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003955993,0.0004038296,0.000427281,0.0003084067,0.000176814,0.0002128413,0.0003612697,0.0001589531,0.00005046509],"category_scores_gemma":[0.000004275491,0.0004129021,0.0002451766,0.0004746163,0.0003075208,0.0004713084,0.00002328069,0.0003076022,0.00001881846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007386442,"about_ca_system_score_gemma":0.0001132502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001322661,"about_ca_topic_score_gemma":0.0001059608,"domain_scores_codex":[0.9976782,0.00006622965,0.0005213537,0.0007115094,0.0002297607,0.0007929396],"domain_scores_gemma":[0.9990112,0.00006329091,0.0001838603,0.0004206614,0.000200611,0.0001204026],"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":[0.0002327728,0.002442884,0.1762176,0.00002024662,0.00009262006,0.000006910574,0.0001631687,0.00006750596,0.7687564,0.00545626,0.001301507,0.04524207],"study_design_scores_gemma":[0.006301354,0.0009751185,0.06817981,0.0001280116,0.00006916247,0.000005993433,0.0008653476,0.0006741869,0.8758147,0.03200764,0.01365882,0.001319908],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7055358,0.0001859246,0.2855092,0.00004447926,0.0004893762,0.001241365,0.0006141828,0.0001249774,0.006254763],"genre_scores_gemma":[0.9836815,0.000003432468,0.01519896,0.0001329958,0.0002461308,0.0002169852,0.0003724952,0.00005592342,0.00009157824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2781458,"threshold_uncertainty_score":0.9998323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007759116110804216,"score_gpt":0.2709163221244393,"score_spread":0.2631572060136351,"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."}}