{"id":"W3005648042","doi":"10.1002/jbio.201960222","title":"A waveguide imaging platform for live‐cell TIRF imaging of neurons over large fields of view","year":2020,"lang":"en","type":"article","venue":"Journal of Biophotonics","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; Engineering and Physical Sciences Research Council; Medical Research Council; Horizon 2020 Framework Programme; Medical Research Council Canada; FP7 Ideas: European Research Council; Wellcome Trust; Universitetet i Tromsø; AstraZeneca","keywords":"Total internal reflection fluorescence microscope; Live cell imaging; Microscopy; Hippocampal formation; Microscope; Neuroscience; Fluorescence microscope; Optics; Biology; Physics; Cell; Fluorescence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001970046,0.0001323243,0.0002880807,0.00004662176,0.00002879718,0.000008644864,0.0002744803,0.00007122737,0.00001759683],"category_scores_gemma":[0.0001307575,0.0001208914,0.0002390967,0.00007679229,0.00006094575,0.0000146494,0.0001164355,0.0001581195,3.471186e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000184217,"about_ca_system_score_gemma":0.0001282729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004646871,"about_ca_topic_score_gemma":0.000001596181,"domain_scores_codex":[0.9989653,0.00001384057,0.0005423796,0.0001524074,0.0001260877,0.0002000149],"domain_scores_gemma":[0.9988908,0.00002466911,0.0005768373,0.0001825264,0.0002444037,0.00008079124],"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":[0.0001535297,0.00005969074,0.001569687,0.0001311443,0.00001892683,0.000007845795,0.0001036544,0.00001224609,0.9952201,0.00002324189,0.001613695,0.001086213],"study_design_scores_gemma":[0.0006124864,0.0004088724,0.00006645642,0.00007855854,0.00003919868,0.00002959029,0.0001402023,0.002080255,0.9789209,0.00006777658,0.01744475,0.0001108918],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8175777,0.008256576,0.1723374,0.0006284627,0.0002174593,0.0004261842,0.0001462319,0.00001283974,0.0003970729],"genre_scores_gemma":[0.947768,0.001486584,0.04975183,0.0008511773,0.00008558223,0.000002828202,0.000009794619,0.0000278007,0.00001638585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1301903,"threshold_uncertainty_score":0.4929809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01043848268072066,"score_gpt":0.2808894979175058,"score_spread":0.2704510152367851,"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."}}