{"id":"W4386083906","doi":"10.7554/elife.82566","title":"Statistical inference on representational geometries","year":2023,"lang":"en","type":"article","venue":"eLife","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Deutsche Forschungsgemeinschaft","keywords":"Overfitting; Bootstrapping (finance); Inference; Computer science; Artificial intelligence; Machine learning; Python (programming language); Resampling; Generalization; Toolbox; Big data; Statistical inference; Computational neuroscience; Artificial neural network; Data mining; Mathematics; Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008862432,0.0000518754,0.00005207627,0.0001128162,0.0001002488,0.00004352071,0.00006133167,0.00001891309,0.0001910887],"category_scores_gemma":[0.003119192,0.00004448605,0.00001615205,0.0005139006,0.00005306419,0.00006542325,0.00004152736,0.00008137115,0.001692869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001080853,"about_ca_system_score_gemma":0.00001827631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006322217,"about_ca_topic_score_gemma":0.000001493731,"domain_scores_codex":[0.9991661,0.00002987048,0.00009287541,0.0002174274,0.0003579182,0.0001358625],"domain_scores_gemma":[0.9987341,0.001067119,0.00002296017,0.000109791,0.00001949827,0.00004655919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001319189,0.0001136339,0.01209155,0.00001894918,0.000005358996,0.0002382755,0.0001226489,0.002753919,0.2321954,0.6511664,0.08503798,0.01612399],"study_design_scores_gemma":[0.000740451,0.0005227044,0.6727652,0.00002350927,0.00000699805,0.0000206635,0.00006264212,0.04528315,0.2043522,0.02881023,0.04696849,0.0004438047],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913355,0.000001111447,0.001375528,0.001350125,0.0005767393,0.00007821438,0.00007753268,0.000188506,0.005016724],"genre_scores_gemma":[0.9958544,0.0000217016,0.00004448491,0.001493383,0.00008068053,0.000009194625,0.0000231272,0.000006243029,0.002466764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6606737,"threshold_uncertainty_score":0.9990844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08362729769444643,"score_gpt":0.3547679841868328,"score_spread":0.2711406864923863,"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."}}