{"id":"W2971796508","doi":"10.1101/756023","title":"Hyperspectral super-resolution imaging with far-red emitting fluorophores using a thin-film tunable filter","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Hyperspectral imaging; Optics; Fluorophore; Materials science; Microscopy; Spectral resolution; Image resolution; Multispectral image; Physics; Computer science; Fluorescence; Computer vision; Artificial intelligence; Spectral line","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":[0.0004105057,0.0008347864,0.0006088981,0.000219497,0.0002604969,0.0002496044,0.0007586576,0.0006294414,0.00002186055],"category_scores_gemma":[0.0001485704,0.0008507098,0.0002096363,0.0002815355,0.0002627407,0.00004101219,0.0008869961,0.0008496847,0.00001293034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003711421,"about_ca_system_score_gemma":0.000747385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001417439,"about_ca_topic_score_gemma":0.000004259603,"domain_scores_codex":[0.9962843,0.0001369969,0.0005311338,0.001673829,0.0003735372,0.001000191],"domain_scores_gemma":[0.9970447,0.00001677757,0.0004298082,0.00174984,0.0005557181,0.0002031408],"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":[0.0001184702,0.00007108371,0.01252547,0.0001807073,0.0001066509,0.0000375303,0.00001162287,0.002603145,0.9838321,0.0000274242,0.0004843839,0.000001471661],"study_design_scores_gemma":[0.0004924878,0.0001151301,0.002938786,0.0005339415,0.0001078128,3.938223e-7,0.00001602167,0.008237417,0.9832687,0.000001799699,0.003174758,0.001112726],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9102591,0.003577365,0.08371186,0.0001092693,0.0005480306,0.001154551,0.0002351076,0.0003846919,0.00002002337],"genre_scores_gemma":[0.8112673,0.0002643144,0.1873641,0.0002359311,0.0004832479,0.0001038407,0.000005643215,0.0002561505,0.00001944446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1036522,"threshold_uncertainty_score":0.9993944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009038762419498216,"score_gpt":0.2314531846643287,"score_spread":0.2224144222448304,"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."}}