{"id":"W2062109930","doi":"10.1016/j.jneumeth.2014.01.011","title":"Isolating specific cell and tissue compartments from 3D images for quantitative regional distribution analysis using novel computer algorithms","year":2014,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Neuroscience and Neuropharmacology Research","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Queen's University","funders":"Canadian Institutes of Health Research; Health Canada","keywords":"Immunolabeling; Biology; Compartment (ship); Neuropil; Computer science; Neuroscience; Pathology; Central nervous system","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":[],"consensus_categories":[],"category_scores_codex":[0.002094714,0.0002284026,0.0005315727,0.0004140309,0.0006425041,0.0002778949,0.0006458173,0.00005359108,0.00000786996],"category_scores_gemma":[0.001018663,0.0001914096,0.0001722215,0.001495408,0.0007868292,0.0007445015,0.000195725,0.0004070858,0.000001140622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004620519,"about_ca_system_score_gemma":0.00007018934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004610075,"about_ca_topic_score_gemma":1.46945e-7,"domain_scores_codex":[0.9962198,0.001218204,0.0006428635,0.0007477851,0.0006717597,0.0004996248],"domain_scores_gemma":[0.9950735,0.003496161,0.0007343949,0.0002117546,0.0002189251,0.0002652774],"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":[0.0000820709,0.0001582661,0.000326554,0.00000652986,0.000004974861,0.00001591606,0.00008222186,0.00224062,0.9919241,0.00007791448,0.0000898184,0.00499101],"study_design_scores_gemma":[0.0006620184,0.0007137787,0.004053474,0.000009546489,0.00008977728,0.00009137137,0.00001583075,0.4021935,0.5882717,0.0001603214,0.003593269,0.0001454035],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3815697,0.00003187072,0.6173009,0.0001569728,0.0007188346,0.0001481484,0.00005766223,0.000009191062,0.000006734278],"genre_scores_gemma":[0.4242228,0.0001235762,0.5741649,0.001153534,0.0002764378,0.000004367981,0.000001478624,0.00002052302,0.00003232739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4036524,"threshold_uncertainty_score":0.7805458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2546513048963229,"score_gpt":0.4924886718702534,"score_spread":0.2378373669739305,"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."}}