{"id":"W2097615118","doi":"10.1002/mrm.24950","title":"Spatial encoding using the nonlinear field perturbations from magnetic materials","year":2013,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Hospital; University of Toronto; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research","keywords":"Encoding (memory); Nonlinear system; Magnetic field; SIGNAL (programming language); Computer science; Field (mathematics); Proof of concept; Algorithm; Biological system; Computer vision; Physics; Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003718788,0.0003725784,0.0001290611,0.0001226028,0.0001063165,0.0002096769,0.0002840696,0.0002445534,0.0005537186],"category_scores_gemma":[0.0006439108,0.000105683,0.0001033119,0.00008928793,0.0003897553,0.0004445136,0.0002783304,0.0002240465,0.000186802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002605695,"about_ca_system_score_gemma":0.0002609852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001588153,"about_ca_topic_score_gemma":0.0001626951,"domain_scores_codex":[0.9999064,0.00001594487,0.000004079237,0.00001410717,0.00004890277,0.00001053599],"domain_scores_gemma":[0.9995539,0.0001638595,0.0001439013,0.0000390875,0.00007485082,0.00002438468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001347572,0.00002761749,0.000153454,0.00009803219,0.000003225462,0.0001276467,0.00003815909,0.002705897,0.9907027,0.001058751,0.00009361132,0.00485617],"study_design_scores_gemma":[0.0000197934,0.0003517924,0.0002937541,0.000005792854,0.000005251696,0.0002098309,0.000009534657,0.01220743,0.985045,0.0001396457,0.001704931,0.000007143989],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8186415,0.000802135,0.1766124,0.0002877939,0.00008119848,0.0002153692,0.0000869723,0.0004193345,0.002853351],"genre_scores_gemma":[0.9222327,0.0002607736,0.07625813,0.00005583635,0.00001480846,0.00006078286,0.00005210832,0.00002415705,0.001040828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005537186,"threshold_uncertainty_score":0.001966655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02291868107811471,"score_gpt":0.2583980224490223,"score_spread":0.2354793413709076,"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."}}