{"id":"W4393810457","doi":"10.5281/zenodo.3606485","title":"Temporal super-resolution microscopy using a hue-encoded shutter","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute in Oncology and Hematology","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Hue; Shutter; Microscopy; Resolution (logic); Optics; Artificial intelligence; Computer vision; Materials science; Computer science; Physics","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":[0.00137245,0.003284708,0.002363064,0.002847173,0.001437403,0.003471525,0.004784155,0.003439579,0.1381463],"category_scores_gemma":[0.005584404,0.001071672,0.002026498,0.005078979,0.0005837376,0.001851206,0.003268631,0.002662527,0.1797055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001565001,"about_ca_system_score_gemma":0.00223045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009995529,"about_ca_topic_score_gemma":0.02094193,"domain_scores_codex":[0.9987105,0.0001765615,0.0001254818,0.0004161638,0.0003736158,0.0001977126],"domain_scores_gemma":[0.9976277,0.0008628577,0.0001903901,0.0006831324,0.000442848,0.000193103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001889012,0.00002917067,0.0006160816,0.00238611,0.00008475305,0.00004503794,0.00002549665,0.0007367301,0.001130959,0.001028226,0.9889265,0.004801926],"study_design_scores_gemma":[0.0003875212,0.0000325211,0.002382664,0.0005633692,0.00009171953,0.0001013241,0.00003823118,0.001149652,0.003150749,0.005133313,0.9868961,0.00007271024],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001171348,0.0002051152,0.0003377077,0.00006432948,0.0000367435,0.00001350613,0.9962141,0.00218831,0.0008230456],"genre_scores_gemma":[0.00057033,0.0001558246,0.001076386,0.00008419954,0.000006777126,0.00009367551,0.9968001,0.0005834921,0.000629252],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1381463,"threshold_uncertainty_score":0.4621455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03717579643371493,"score_gpt":0.337653278941787,"score_spread":0.3004774825080721,"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."}}