{"id":"W2811450527","doi":"10.6084/m9.figshare.6724625","title":"A standardised representation for non-parametric fMRI results","year":2018,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Metadata; Computer science; JSON; Representation (politics); Information retrieval; Flemish; Functional magnetic resonance imaging; Data science; Data curation; Process (computing); Neuroimaging; Data mining; World Wide Web; Programming language; Geography; 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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00361162,0.001216862,0.000697761,0.003700229,0.000426652,0.002848927,0.001426583,0.001538026,0.03442555],"category_scores_gemma":[0.02442602,0.0004788045,0.00109002,0.003180531,0.0007257115,0.00242941,0.001771438,0.001472429,0.01936993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005262187,"about_ca_system_score_gemma":0.001278499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001226273,"about_ca_topic_score_gemma":0.001385633,"domain_scores_codex":[0.9977909,0.0007008364,0.0005417529,0.0003534029,0.0004741988,0.0001387334],"domain_scores_gemma":[0.9931085,0.001983618,0.0006616131,0.002335546,0.001736837,0.0001738045],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00120757,0.0002416191,0.002122106,0.001395633,0.0001499465,0.0008494901,0.0004045487,0.01821966,0.03080392,0.0640344,0.1637237,0.7168474],"study_design_scores_gemma":[0.0003887891,0.0008387848,0.009533373,0.000861842,0.0003734132,0.004200873,0.000384233,0.2953128,0.04316798,0.2200433,0.4245648,0.0003297957],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005969735,0.0004803365,0.9459043,0.0006565672,0.0007920762,0.0006074521,0.02128862,0.01536338,0.008937663],"genre_scores_gemma":[0.1306462,0.0009394113,0.8022268,0.0004872112,0.0005985324,0.002947706,0.04565231,0.00390618,0.01259562],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9963884,"threshold_uncertainty_score":0.1151649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508626525216942,"score_gpt":0.2963507870041821,"score_spread":0.2812645217520127,"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."}}