{"id":"W1922680067","doi":"10.1002/mrm.25148","title":"SHARP edges: Recovering cortical phase contrast through harmonic extension","year":2014,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced X-ray Imaging Techniques","field":"Physics and Astronomy","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Alberta; Deutsche Forschungsgemeinschaft; Friedrich-Schiller-Universität Jena","keywords":"Contrast (vision); RADIUS; Phase (matter); Harmonic; Field (mathematics); Physics; Kernel (algebra); Artifact (error); Local field potential; Mathematics; Enhanced Data Rates for GSM Evolution; Mathematical analysis; Optics; Geometry; Computer science; Acoustics; Artificial intelligence; Neuroscience","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":[0.0004966247,0.0006461171,0.0002560284,0.0005010733,0.0001480696,0.0004371058,0.0006201247,0.0004081351,0.001501302],"category_scores_gemma":[0.001795929,0.0002967378,0.000442927,0.0002824679,0.000495475,0.0007375259,0.0009426321,0.0006684214,0.0004310177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002274542,"about_ca_system_score_gemma":0.0004695224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000610211,"about_ca_topic_score_gemma":0.0009595279,"domain_scores_codex":[0.9998959,0.00001988006,0.000004854685,0.00001797789,0.00004970202,0.00001165114],"domain_scores_gemma":[0.9997198,0.0001086582,0.00005036465,0.00005803136,0.00004389683,0.00001924253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000706443,0.0001202691,0.001965711,0.0005595461,0.0001628242,0.0004845512,0.0002576394,0.2484547,0.3886759,0.02246326,0.002205147,0.3339441],"study_design_scores_gemma":[0.00007319236,0.0003191383,0.00170106,0.00003910478,0.00005796926,0.0006338383,0.00005241138,0.8161361,0.1657384,0.01025395,0.004953144,0.00004172371],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04603085,0.0001977641,0.9518946,0.00006720819,0.00002780891,0.00006547124,0.00006429204,0.0004564564,0.001195519],"genre_scores_gemma":[0.3668905,0.0005056726,0.6290141,0.00008842342,0.00003772283,0.00008778326,0.0002026033,0.0002113919,0.002961827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001501302,"threshold_uncertainty_score":0.005022347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02485181444256856,"score_gpt":0.3208902976076194,"score_spread":0.2960384831650508,"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."}}