{"id":"W4293463483","doi":"10.1038/s41597-022-01625-7","title":"Le Petit Prince multilingual naturalistic fMRI corpus","year":2022,"lang":"en","type":"article","venue":"Scientific Data","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Office of Naval Research; National Institute on Aging; Multidisciplinary University Research Initiative; Jiangsu University; National Institutes of Health; National Science Foundation; Jiangsu Normal University; New York University Abu Dhabi; Agence Nationale de la Recherche","keywords":"Functional magnetic resonance imaging; Generalizability theory; Computer science; Psychology; Active listening; Functional neuroimaging; Neuroimaging; Cognitive neuroscience; Cognitive psychology; Cognition; Communication","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009556716,0.0001365494,0.0001411825,0.0001304153,0.001523035,0.0002183418,0.003143674,0.00002865742,0.0004980888],"category_scores_gemma":[0.001670811,0.000130447,0.00004087113,0.0005681749,0.0006378675,0.000269808,0.003563897,0.000405754,0.0001972791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002308336,"about_ca_system_score_gemma":0.0003184505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006343797,"about_ca_topic_score_gemma":0.00008151602,"domain_scores_codex":[0.9971703,0.0002777219,0.00023506,0.001424117,0.0004825209,0.0004103059],"domain_scores_gemma":[0.9973838,0.0002509718,0.0001200574,0.002125835,0.00003111068,0.00008825228],"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":[0.00003609852,0.0002311523,0.00005196069,0.00001487781,0.000003569912,0.001709977,0.0005719818,0.00004539074,0.9615429,0.002089394,0.02878276,0.004919955],"study_design_scores_gemma":[0.0004951777,0.0000571278,0.00005545193,0.000006353642,0.00001288514,0.001076713,0.000352858,0.001095443,0.161594,0.000540994,0.8343932,0.0003197134],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814145,0.0001962642,0.0000156,0.001229075,0.007675426,0.0003325939,0.005882087,0.0002436852,0.003010764],"genre_scores_gemma":[0.9801985,0.000002888573,0.0002173025,0.0009474398,0.00009863745,0.0000104993,0.001147527,0.00001643035,0.01736074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8056105,"threshold_uncertainty_score":0.9997768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07188631091391356,"score_gpt":0.31624786866279,"score_spread":0.2443615577488764,"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."}}