{"id":"W2110220245","doi":"10.1109/iembs.2007.4352742","title":"Impact of realignment on spinal functional MRI time series","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Functional magnetic resonance imaging; Magnetic resonance imaging; Variance (accounting); Computer science; Noise (video); Spinal cord; Series (stratigraphy); Communication noise; Artificial intelligence; Motion (physics); Pattern recognition (psychology); Image (mathematics); Neuroscience; Medicine; Radiology; Geology; Psychology","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":[0.00667837,0.001657503,0.001199786,0.0009467278,0.0006137319,0.001357529,0.0006812289,0.001872398,0.002318839],"category_scores_gemma":[0.06830432,0.0004630488,0.0009072976,0.001365484,0.001024589,0.001491762,0.001295377,0.001590939,0.0009200172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004927527,"about_ca_system_score_gemma":0.0007674916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003782199,"about_ca_topic_score_gemma":0.004092398,"domain_scores_codex":[0.9951566,0.002080655,0.0004376284,0.0009163265,0.001160327,0.0002483746],"domain_scores_gemma":[0.9686421,0.02435262,0.001768104,0.003275244,0.001534921,0.0004270129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.008897756,0.0006498212,0.02056065,0.001408852,0.0008640308,0.001509587,0.0004520711,0.3611251,0.2004344,0.002399927,0.005545157,0.3961525],"study_design_scores_gemma":[0.0002442896,0.003155739,0.1201842,0.0002396853,0.000696809,0.003937078,0.0003821655,0.7227604,0.1327372,0.005909527,0.009474708,0.0002782971],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6976862,0.007329246,0.2814959,0.002896603,0.00150086,0.0001635359,0.001984874,0.004426329,0.002516412],"genre_scores_gemma":[0.9404779,0.001489243,0.05014509,0.0004642058,0.0002435736,0.000091005,0.00337764,0.001404193,0.002307053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00667837,"threshold_uncertainty_score":0.03531903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03234900145902697,"score_gpt":0.3440387792197181,"score_spread":0.3116897777606912,"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."}}