{"id":"W3095789873","doi":"10.1101/2020.10.31.363507","title":"Focus-tunable microscope for imaging small neuronal processes in freely moving animals","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Université Laval; Centre hospitalier de l'Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Microscope; Calcium imaging; Magnification; Lens (geology); Materials science; Focus (optics); Frame rate; Neuroimaging; Microscopy; Optics; Two-photon excitation microscopy; Biomedical engineering; Computer science; Physics; Calcium; Neuroscience; Medicine; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002974621,0.0006982452,0.0006043421,0.0001801743,0.000135014,0.0002204433,0.0009676425,0.0004864738,0.000005644308],"category_scores_gemma":[0.0008633471,0.0008388902,0.0001601282,0.0003567461,0.0001622529,0.00001956295,0.001070106,0.0006050952,0.000005684481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001385995,"about_ca_system_score_gemma":0.001116446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004863439,"about_ca_topic_score_gemma":0.00001881387,"domain_scores_codex":[0.9965839,0.00007604742,0.0006303454,0.001711155,0.0001785865,0.0008199692],"domain_scores_gemma":[0.9978623,0.00003784995,0.0004180308,0.0009288592,0.0005418346,0.0002110657],"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":[0.0001302712,0.00007348308,0.007564509,0.0008773338,0.00003493686,0.00002262046,0.000006665821,0.00004165615,0.990772,0.00004248184,0.0004285462,0.000005496323],"study_design_scores_gemma":[0.0004728036,0.0001291107,0.005335703,0.000443563,0.00003853262,3.77996e-8,0.000003335851,0.0001536318,0.9848459,0.00001883099,0.007719315,0.0008392194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.819622,0.008904879,0.1662317,0.000652652,0.0004894082,0.002743669,0.0008822543,0.000460799,0.0000126618],"genre_scores_gemma":[0.8273187,0.0005488695,0.1702137,0.0004756722,0.0004567248,0.000724488,0.000003525697,0.0002542904,0.000004119566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00835601,"threshold_uncertainty_score":0.9994062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01398687541343486,"score_gpt":0.2482222676576414,"score_spread":0.2342353922442065,"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."}}