{"id":"W4393576933","doi":"10.5281/zenodo.3606484","title":"Temporal super-resolution microscopy using a hue-encoded shutter","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","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); Computer vision; Artificial intelligence; Optics; Computer graphics (images); Computer science; Geology; 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":[0.001087933,0.003050508,0.002067712,0.002429364,0.001033858,0.002618308,0.00384628,0.003036492,0.05956472],"category_scores_gemma":[0.003434521,0.0007902499,0.001777823,0.004045389,0.0005247676,0.001345989,0.002509404,0.002003239,0.1010366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001332948,"about_ca_system_score_gemma":0.001744918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01054239,"about_ca_topic_score_gemma":0.02197152,"domain_scores_codex":[0.9989398,0.0001509689,0.0001013116,0.0003537047,0.0002995706,0.00015459],"domain_scores_gemma":[0.9986359,0.0003740535,0.0001307759,0.0004395334,0.0003002923,0.0001194411],"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":[0.0002669991,0.00005309077,0.000965348,0.002324823,0.0001136127,0.00006795687,0.0000257677,0.001450089,0.001612456,0.001102099,0.9841852,0.007832462],"study_design_scores_gemma":[0.0003757627,0.00004524144,0.003527703,0.0005415329,0.0001052848,0.0001732768,0.0000516307,0.002265138,0.004067474,0.005491355,0.9832721,0.00008348534],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002584348,0.0003163331,0.00048582,0.00007394762,0.0000394907,0.00001742589,0.996075,0.001861265,0.0008722247],"genre_scores_gemma":[0.0007075714,0.0001378944,0.001047644,0.00005760508,0.000005845396,0.00007345337,0.9971889,0.0002473532,0.0005337643],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05956472,"threshold_uncertainty_score":0.1992639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03370788160948385,"score_gpt":0.3050549384353792,"score_spread":0.2713470568258953,"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."}}