{"id":"W2943805928","doi":"10.1016/j.bpj.2019.04.029","title":"Registration and Visualization of Correlative Super-Resolution Microscopy Data","year":2019,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Deutsche Forschungsgemeinschaft","keywords":"Microscopy; Alexa Fluor; Visualization; Fiducial marker; Artificial intelligence; Computer science; Computer vision; Fluorescence microscope; Resolution (logic); Optical sectioning; Pattern recognition (psychology); Fluorescence; Optics; 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":[0.000725193,0.0003553922,0.0003965071,0.001461838,0.0003830881,0.001258828,0.0006967537,0.0004634376,0.004283919],"category_scores_gemma":[0.002600807,0.0004291522,0.0003890727,0.001909981,0.000390786,0.0007860992,0.001041095,0.001066021,0.001337808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000419848,"about_ca_system_score_gemma":0.001162084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009890104,"about_ca_topic_score_gemma":0.001801603,"domain_scores_codex":[0.9995909,0.00005930144,0.00002953308,0.00005633583,0.0002156048,0.00004833935],"domain_scores_gemma":[0.9987142,0.0002618404,0.0001963023,0.0005241528,0.0002403654,0.00006314761],"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":[0.000408033,0.0001157256,0.001506167,0.000262989,0.00005518046,0.0002729158,0.000448804,0.01319066,0.789711,0.00997924,0.00326963,0.1807797],"study_design_scores_gemma":[0.00003366971,0.0000926338,0.01253861,0.00003804962,0.00004938706,0.001017308,0.0001616043,0.2312873,0.7330482,0.00685856,0.01481178,0.00006301817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09354093,0.0002455522,0.8988144,0.0002680545,0.00005755868,0.0001363304,0.0005949503,0.003432728,0.002909445],"genre_scores_gemma":[0.3419823,0.0005048842,0.6519746,0.00007628425,0.00004944926,0.0002811046,0.001103185,0.001137589,0.00289066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004283919,"threshold_uncertainty_score":0.0143311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01319593276420228,"score_gpt":0.3103240655798243,"score_spread":0.297128132815622,"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."}}