{"id":"W1975999871","doi":"10.1016/j.neuroimage.2008.08.040","title":"Development and validation of retrospective spinal cord motion time-course estimates (RESPITE) for spin-echo spinal fMRI: Improved sensitivity and specificity by means of a motion-compensating general linear model analysis","year":2008,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Spinal cord; Communication noise; Functional magnetic resonance imaging; Neuroscience; Computer science; Artificial intelligence; 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":[0.0110599,0.0009164085,0.0006284359,0.001223152,0.0003408401,0.0008449403,0.0008425259,0.001065069,0.0006666963],"category_scores_gemma":[0.02158037,0.0007849446,0.0006896129,0.0004797516,0.0003519536,0.0008583353,0.0007411746,0.0007183518,0.0005064196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002189565,"about_ca_system_score_gemma":0.0007988475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001195719,"about_ca_topic_score_gemma":0.003241842,"domain_scores_codex":[0.9980224,0.001097161,0.000160252,0.0004374528,0.0002263374,0.00005639777],"domain_scores_gemma":[0.988633,0.007738754,0.0007205504,0.001038009,0.001733243,0.0001364217],"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":[0.003411173,0.0005828365,0.07268835,0.0005254583,0.001278435,0.0003120481,0.0006135489,0.06057169,0.2369416,0.002437091,0.001325004,0.6193128],"study_design_scores_gemma":[0.0003040031,0.001592024,0.09239325,0.00004073254,0.0008160645,0.001030371,0.0001128226,0.7640064,0.1347742,0.001388105,0.003399562,0.0001425942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1996377,0.0003411106,0.797806,0.00004372975,0.00003161297,0.0001920717,0.000382486,0.001186313,0.000378987],"genre_scores_gemma":[0.443747,0.0002172106,0.5534496,0.00004610645,0.00002075089,0.0002916571,0.00114247,0.00042009,0.0006651348],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0110599,"threshold_uncertainty_score":0.05849099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03566643179779388,"score_gpt":0.3299683098096943,"score_spread":0.2943018780119004,"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."}}