{"id":"W2006948821","doi":"10.1073/pnas.191361398","title":"Digital in-line holography for biological applications","year":2001,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Digital Holography and Microscopy","field":"Physics and Astronomy","cited_by":572,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Digital holography; Holography; Digital holographic microscopy; Biological specimen; Computer science; Microscopy; Optics; Line (geometry); Iterative reconstruction; Computer vision; Artificial intelligence; Computer graphics (images); Materials science; Physics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004160711,0.0003940011,0.0003336027,0.0004814998,0.0002574155,0.0006702269,0.0006336026,0.0003257683,0.01422245],"category_scores_gemma":[0.0007995238,0.0002505138,0.0002302798,0.0006744845,0.0003577813,0.0007673432,0.0006571818,0.0008036918,0.00437813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003782234,"about_ca_system_score_gemma":0.0005086799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003278767,"about_ca_topic_score_gemma":0.0007212617,"domain_scores_codex":[0.9997945,0.00003116519,0.00001247477,0.00002035359,0.0001279007,0.00001357648],"domain_scores_gemma":[0.9996506,0.0001014066,0.00002249362,0.0001171084,0.00007945266,0.00002889845],"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":[0.0001898833,0.00008232071,0.0006125306,0.0007368618,0.0000286011,0.0001086098,0.0001526845,0.003099786,0.3360153,0.03713026,0.02121022,0.6006331],"study_design_scores_gemma":[0.0001116793,0.0002627179,0.00203976,0.000142093,0.0000828244,0.000933704,0.00007234186,0.05944731,0.3926582,0.02515934,0.5190361,0.00005396662],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01320412,0.00594623,0.9452954,0.000645668,0.0003658731,0.0001404968,0.0005596004,0.008284847,0.02555778],"genre_scores_gemma":[0.1055738,0.006681588,0.8698796,0.0003135433,0.0001522632,0.0001785006,0.001325961,0.000749508,0.01514526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01422245,"threshold_uncertainty_score":0.04757881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04522935681710434,"score_gpt":0.3243316630636965,"score_spread":0.2791023062465922,"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."}}