{"id":"W2167577015","doi":"10.1002/mrm.20215","title":"Multiple mouse biological loading and monitoring system for MRI","year":2004,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"University of Toronto","keywords":"Computer science; Nuclear magnetic resonance; Physics","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":[0.0001919635,0.0001257801,0.0003105753,0.00007916526,0.00005988164,0.000003936514,0.00006674563,0.000085503,0.000005566441],"category_scores_gemma":[0.0001916985,0.00009252662,0.00002383429,0.000168718,0.0001494954,0.00002579943,0.00002657014,0.0001386461,0.000001844917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001099637,"about_ca_system_score_gemma":0.00001616031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006910608,"about_ca_topic_score_gemma":0.000004876414,"domain_scores_codex":[0.9990636,0.000008175857,0.0002869608,0.000290955,0.0001137135,0.0002365993],"domain_scores_gemma":[0.9994738,0.0001253897,0.00004627807,0.0002113332,0.00004602432,0.00009723144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009209175,0.000419938,0.2212411,0.0009539626,0.000008537322,0.0001965444,0.001515465,0.0003607223,0.355193,0.01890573,0.0007391539,0.399545],"study_design_scores_gemma":[0.05080257,0.01163641,0.4330273,0.01420265,0.0002053095,0.000956208,0.00883263,0.008396658,0.1827372,0.01139905,0.2763192,0.001484859],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9004686,0.01963713,0.07237971,0.004356409,0.0001196338,0.002064666,0.000009869463,0.000277384,0.0006866023],"genre_scores_gemma":[0.904761,0.001673454,0.09250244,0.0001007155,0.0002779409,0.0004334492,0.000006816298,0.00001750701,0.0002266309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3980601,"threshold_uncertainty_score":0.3773126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03970556124456874,"score_gpt":0.329105762926873,"score_spread":0.2894002016823042,"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."}}