{"id":"W4391164504","doi":"10.1101/2024.01.24.577045","title":"A Deep Learning Pipeline for Mapping in situ Network-level Neurovascular Coupling in Multi-photon Fluorescence Microscopy","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"Alliance de recherche numérique du Canada","keywords":"Photostimulation; Optogenetics; Microcirculation; Premovement neuronal activity; Anatomy; Biomedical engineering; Neuroscience; Chemistry; Biophysics; Biology; Medicine; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007493939,0.001226603,0.0006543532,0.0008098403,0.0004538863,0.001006067,0.001854935,0.001258865,0.003254113],"category_scores_gemma":[0.001640638,0.0007440237,0.001164859,0.0006906244,0.0004719705,0.0007743145,0.001175914,0.001827131,0.001205847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456148,"about_ca_system_score_gemma":0.001399389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01217637,"about_ca_topic_score_gemma":0.01608281,"domain_scores_codex":[0.9997846,0.00003967364,0.000009554427,0.00008312156,0.00004603613,0.00003702828],"domain_scores_gemma":[0.9995827,0.000184052,0.0000548934,0.00005143251,0.00008903639,0.00003791741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002546845,0.0001761976,0.002258516,0.0002393518,0.0002318595,0.0002025279,0.0001082847,0.7572854,0.03531062,0.004904154,0.007519537,0.1915088],"study_design_scores_gemma":[0.00000401121,0.00001289735,0.0001568258,0.000002976958,0.000004388278,0.000008775306,0.000004252483,0.9959637,0.001993911,0.00152001,0.0003237386,0.000004550523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02582686,0.0002518139,0.9619305,0.0003064367,0.00004155202,0.00008259052,0.000828606,0.009872925,0.0008588166],"genre_scores_gemma":[0.3939964,0.0003343079,0.5975152,0.0003193722,0.00004316919,0.0004121455,0.002955311,0.0007075123,0.003716552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01217637,"threshold_uncertainty_score":0.02421099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02566665198866432,"score_gpt":0.2846824868431035,"score_spread":0.2590158348544392,"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."}}