{"id":"W3016528875","doi":"10.1364/boe.390782","title":"Imaging intracellular motion with dynamic micro-optical coherence tomography","year":2020,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Massachusetts General Hospital; Remondi Family Foundation","keywords":"Optical coherence tomography; Intracellular; Coherence (philosophical gambling strategy); Pixel; Optics; Contrast (vision); Computer science; Physics; Biology; Cell biology","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":[0.00009353662,0.0002939014,0.0002730372,0.0001284107,0.00007963718,0.0001035201,0.0004565386,0.0001466706,0.0001097922],"category_scores_gemma":[0.00004071663,0.0002662716,0.00009788715,0.0009472293,0.0005502973,0.0001670116,0.00007578213,0.000465532,0.0001009315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003378788,"about_ca_system_score_gemma":0.00002325903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002131747,"about_ca_topic_score_gemma":7.368722e-7,"domain_scores_codex":[0.9981787,0.00002189126,0.0003677759,0.0004360233,0.000479967,0.0005157084],"domain_scores_gemma":[0.9987341,0.00009920992,0.00004034487,0.0003679932,0.00008380522,0.0006745831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006714974,0.0003755908,0.001690433,0.0004230085,0.0002066535,0.0001507529,0.0005539119,0.00200645,0.9526912,0.005210149,0.001497582,0.03512715],"study_design_scores_gemma":[0.002506138,0.0005514979,0.004132851,0.0002823393,0.0003177581,0.00007748041,0.0005464073,0.8949344,0.07652644,0.001430963,0.01655479,0.00213895],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1680476,0.0006167517,0.8243116,0.002162655,0.0001692833,0.0006087027,0.00007210046,0.001265484,0.00274583],"genre_scores_gemma":[0.9281101,0.00003451933,0.07135614,0.0001597984,0.0001097998,0.0000984666,0.00006805562,0.00005732725,0.000005801038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8929279,"threshold_uncertainty_score":0.999979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00722556007625264,"score_gpt":0.2083650503951572,"score_spread":0.2011394903189045,"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."}}