{"id":"W4393732393","doi":"10.5281/zenodo.10002180","title":"A database of the healthy human spinal cord morphometry in the PAM50 template space","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Spinal cord; Space (punctuation); Computer science; Database; Neuroscience; Biology; Operating 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":[0.0009440337,0.003031387,0.001879723,0.002915464,0.0007822911,0.001746937,0.003645478,0.003254715,0.0347208],"category_scores_gemma":[0.004446138,0.0006300209,0.001602385,0.003373278,0.0006252004,0.0008265167,0.002323756,0.001581637,0.06949957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009308157,"about_ca_system_score_gemma":0.001640728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01060449,"about_ca_topic_score_gemma":0.02174144,"domain_scores_codex":[0.9990839,0.0001436139,0.0001278635,0.0002914447,0.0002443067,0.0001088293],"domain_scores_gemma":[0.9985494,0.0003235666,0.0001243986,0.0005059014,0.0003571605,0.0001396539],"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":[0.0003247722,0.0001277087,0.002261784,0.001631928,0.0001366739,0.0002767315,0.00005660005,0.0009745439,0.001311079,0.0005440862,0.9744869,0.0178673],"study_design_scores_gemma":[0.0007952673,0.0002808201,0.03243586,0.001169128,0.0002978219,0.002981702,0.0002539847,0.005906899,0.005565129,0.005317725,0.9447751,0.0002206094],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001942688,0.0004882002,0.0009112268,0.0001252773,0.00007778416,0.00008502823,0.9934574,0.001953759,0.0009587213],"genre_scores_gemma":[0.00224748,0.0001291315,0.001026112,0.00006257432,0.00001449674,0.0002078383,0.9955979,0.0001135004,0.000600916],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0347208,"threshold_uncertainty_score":0.1161527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05562861222058294,"score_gpt":0.3002172969084189,"score_spread":0.2445886846878359,"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."}}