{"id":"W4412785599","doi":"","title":"Monitoring morphometric drift in lifelong learning segmentation of the spinal cord.","year":2025,"lang":"en","type":"preprint","venue":"PubMed","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Canadian Institute for Advanced Research; University of British Columbia; Mila - Quebec Artificial Intelligence Institute; University of Toronto; University Health Network; Université de Montréal; Praxis Spinal Cord Institute; St. Michael's Hospital; Polytechnique Montréal","funders":"","keywords":"Spinal cord; Segmentation; Lifelong learning; Artificial intelligence; Computer science; Cartography; Psychology; Geography; Neuroscience; Pedagogy","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":[0.002358703,0.0007912495,0.0006626586,0.001171187,0.0003354499,0.001128275,0.001584894,0.001338951,0.001242164],"category_scores_gemma":[0.006804031,0.0003464866,0.0006108778,0.0006069665,0.0005212454,0.0007489566,0.0009779087,0.0006802318,0.0005886127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001126488,"about_ca_system_score_gemma":0.00128204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009503337,"about_ca_topic_score_gemma":0.0136826,"domain_scores_codex":[0.9994311,0.0001126648,0.00003610934,0.0002183214,0.0001560122,0.00004580801],"domain_scores_gemma":[0.99879,0.0004802093,0.0001923781,0.0001958195,0.000254993,0.00008652712],"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":[0.0009312485,0.0003506703,0.01697197,0.0005893836,0.0004279331,0.0004316217,0.0005787456,0.5835583,0.0531803,0.002460395,0.006756527,0.333763],"study_design_scores_gemma":[0.00001750874,0.0001858451,0.004231054,0.000030204,0.00004721099,0.0002641795,0.00004570395,0.9775996,0.0140164,0.001723787,0.001806336,0.00003216079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2694256,0.001222597,0.7136834,0.0004528384,0.0001823541,0.000310931,0.002024727,0.01116048,0.001536948],"genre_scores_gemma":[0.7512811,0.0004217997,0.2418866,0.0001946925,0.00004777135,0.0002841875,0.003339305,0.0007676498,0.001776968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009503337,"threshold_uncertainty_score":0.01889604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02273503713183497,"score_gpt":0.2548911094055117,"score_spread":0.2321560722736767,"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."}}