{"id":"W2560607027","doi":"10.1083/jcb.2156pi","title":"Rusty Gage: A plastic approach to neuroscience","year":2016,"lang":"en","type":"article","venue":"The Journal of Cell Biology","topic":"Neuroscience, Education and Cognitive Function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Biology; Neuroscience; Computational biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0005820537,0.00009635794,0.0001191169,0.0001504814,0.0001892264,0.0000222034,0.0005958,0.00003064753,0.00004214465],"category_scores_gemma":[0.002496993,0.00004335145,0.0000480932,0.0004506528,0.0003650031,0.0001445897,0.00007067126,0.0001609073,0.000105845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002722654,"about_ca_system_score_gemma":0.0001209491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.352185e-7,"about_ca_topic_score_gemma":1.759399e-7,"domain_scores_codex":[0.9986364,0.0004630563,0.0002493421,0.0002027076,0.0001912754,0.0002572526],"domain_scores_gemma":[0.9984723,0.000887431,0.0002390165,0.0001652047,0.00009780593,0.0001381809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007733867,0.00008896225,0.00008542143,0.000001355747,2.237391e-7,0.000001251893,0.0001001852,0.00001509594,0.9967325,0.0006886593,0.0003414054,0.001867615],"study_design_scores_gemma":[0.0003898702,0.0008006544,0.004414689,0.000009154282,0.00001187854,0.000287846,0.00009029663,0.00007034074,0.9627307,0.0007150852,0.03037224,0.0001072676],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9470469,0.00001062654,0.03823541,0.001377354,0.002810081,0.0001247394,0.000004617804,0.0000148977,0.01037536],"genre_scores_gemma":[0.9943653,0.00006413399,0.00005690182,0.00400304,0.0001545642,0.000002679501,3.491671e-8,0.000006222786,0.001347161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04731834,"threshold_uncertainty_score":0.2989314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04655613598119496,"score_gpt":0.2789442305181359,"score_spread":0.232388094536941,"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."}}