{"id":"W4393899702","doi":"10.7554/elife.95525.1.sa1","title":"Reviewer #1 (Public Review): A Deep Learning Pipeline for Mapping in situ Network-level Neurovascular Coupling in Multi-photon Fluorescence Microscopy","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada","keywords":"Two-photon excitation microscopy; In situ; Neurovascular bundle; Fluorescence; Pipeline (software); Microscopy; Fluorescence microscope; Coupling (piping); Multiphoton fluorescence microscope; Materials science; Computer science; Physics; Neuroscience; Optics; Biology; Anatomy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03915114,0.001494376,0.002783964,0.003866452,0.003017643,0.008717295,0.005270218,0.01236227,0.1059832],"category_scores_gemma":[0.267477,0.00131559,0.004239972,0.002760053,0.002082316,0.004687821,0.004486879,0.006170392,0.04572085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005311097,"about_ca_system_score_gemma":0.01600861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005349869,"about_ca_topic_score_gemma":0.007895865,"domain_scores_codex":[0.9746605,0.008715503,0.003291385,0.002754344,0.009264003,0.001314344],"domain_scores_gemma":[0.6230119,0.03859365,0.0160835,0.01086571,0.3000269,0.01141832],"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":[0.00006641847,0.000006896354,0.0001318538,0.001121898,0.00004976643,0.00002642176,0.00002630761,0.00004756166,0.00006540895,0.0001490038,0.9904652,0.007843193],"study_design_scores_gemma":[0.0006259139,0.00008797698,0.001865015,0.005202655,0.0003166209,0.0002531745,0.0001264889,0.0006081473,0.0005534781,0.002243648,0.9880018,0.000115161],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"commentary","genre_scores_codex":[0.0007163799,0.01469235,0.006330245,0.4033863,0.5536997,0.001670789,0.009611947,0.002249609,0.007642661],"genre_scores_gemma":[0.01771903,0.02005454,0.01159195,0.4747727,0.3759813,0.008950983,0.009460531,0.004277229,0.07719176],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.1059832,"threshold_uncertainty_score":0.3545491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06550820937252476,"score_gpt":0.3726923148443468,"score_spread":0.3071841054718221,"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."}}