{"id":"W2076609203","doi":"10.1371/journal.pone.0053678","title":"Reliable Identification of Deep Sulcal Pits: The Effects of Scan Session, Scanner, and Surface Extraction Tool","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Korea Science and Engineering Foundation; National Research Foundation of Korea; National Research Foundation","keywords":"Scanner; Neuroimaging; White matter; Artificial intelligence; Similarity (geometry); Pattern recognition (psychology); Nuclear medicine; Computer science; Magnetic resonance imaging; Biology; Medicine; Radiology; Neuroscience; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009808599,0.0007494661,0.000818307,0.0007572208,0.0005041471,0.0008425986,0.00053645,0.0007232832,0.001095129],"category_scores_gemma":[0.04983279,0.0004678551,0.0006130005,0.0005845738,0.0008548577,0.001115254,0.0009512089,0.0005397175,0.0003820287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001633444,"about_ca_system_score_gemma":0.0003049504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009972707,"about_ca_topic_score_gemma":0.002239421,"domain_scores_codex":[0.9949971,0.002279023,0.0005903801,0.001030672,0.000918005,0.0001847232],"domain_scores_gemma":[0.9501534,0.03092366,0.005968757,0.007416736,0.005051889,0.0004855329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0080336,0.000374901,0.5071495,0.0009141928,0.003189613,0.0009519763,0.003274659,0.009707788,0.2800763,0.000425712,0.001157363,0.1847444],"study_design_scores_gemma":[0.00006275556,0.001793358,0.9346381,0.00004085577,0.0005988889,0.002214354,0.0003852553,0.01479381,0.04317883,0.0009248778,0.001267628,0.0001012445],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542351,0.0009413811,0.04298051,0.0000975195,0.00008864402,0.0001314952,0.0002548206,0.0003425645,0.000927984],"genre_scores_gemma":[0.9907153,0.00009507644,0.008322422,0.00003184753,0.00003134989,0.00005069583,0.0002954796,0.0001776343,0.0002803001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009808599,"threshold_uncertainty_score":0.05187345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03248149711368872,"score_gpt":0.2956015678760396,"score_spread":0.2631200707623509,"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."}}