{"id":"W3128563494","doi":"10.3791/62062","title":"Semi-Quantitative Determination of Dopaminergic Neuron Density in the Substantia Nigra of Rodent Models using Automated Image Analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; University of Toronto; University Health Network","funders":"","keywords":"Substantia nigra; Tyrosine hydroxylase; Dopaminergic; Stereology; Neuroscience; Midbrain; Neuron; Parkinson's disease; Computer science; Biology; Dopamine; Pathology; Medicine; Disease; Central nervous system","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":[0.001527993,0.001181468,0.0007086745,0.003457311,0.0005796805,0.0008846,0.0009964359,0.0008429178,0.002786846],"category_scores_gemma":[0.0008346758,0.0007010715,0.0007023056,0.001083308,0.0007718403,0.0008360713,0.0008443675,0.001438204,0.001129185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004900374,"about_ca_system_score_gemma":0.000664183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198349,"about_ca_topic_score_gemma":0.00258492,"domain_scores_codex":[0.998645,0.0001640306,0.0001069591,0.0002893077,0.000647957,0.0001467354],"domain_scores_gemma":[0.9989849,0.0001638546,0.0002426171,0.0001655002,0.0003762227,0.00006698556],"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":[0.0001175969,0.00010072,0.0005845103,0.0002912711,0.00004110832,0.00008609104,0.0001215305,0.0004238908,0.9805446,0.0006875573,0.0002978919,0.01670315],"study_design_scores_gemma":[0.00003526546,0.0004933822,0.01333937,0.00008941119,0.0001340722,0.0007980852,0.00009682492,0.01293549,0.9631895,0.0008152986,0.007991104,0.00008216577],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2603604,0.004659038,0.7176044,0.0001902148,0.0002038893,0.001591538,0.003025214,0.00499397,0.007371398],"genre_scores_gemma":[0.2349147,0.003883525,0.7452593,0.0001085822,0.00005306531,0.003522283,0.002397756,0.0009889086,0.008871893],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003457311,"threshold_uncertainty_score":0.009322941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03066326941844402,"score_gpt":0.4231167399179266,"score_spread":0.3924534704994826,"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."}}