{"id":"W2134212242","doi":"10.1186/1756-6606-3-9","title":"A quantitative proteomic analysis of long-term memory","year":2010,"lang":"en","type":"article","venue":"Molecular Brain","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"University of Oxford","keywords":"Psychopharmacology; Term (time); Neuroscience; Psychology; Cognitive science; Computational biology; Biology; Psychiatry; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002741435,0.000477303,0.0003235716,0.0009754281,0.0003365881,0.0003487183,0.0003262507,0.0003516057,0.0009185247],"category_scores_gemma":[0.0002939473,0.0001364008,0.0002924939,0.0006055809,0.0002527792,0.0002738901,0.0002235873,0.0004323062,0.0003596063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002864015,"about_ca_system_score_gemma":0.0002154242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004059873,"about_ca_topic_score_gemma":0.0007106741,"domain_scores_codex":[0.9998357,0.00001464738,0.00001448495,0.00004737283,0.00006119688,0.00002647921],"domain_scores_gemma":[0.9997818,0.00003364326,0.00005134551,0.00001413623,0.00008701351,0.00003203794],"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":[0.00007165618,0.00002626327,0.0006469331,0.00007675681,0.00001784357,0.00003066425,0.00001212147,0.00002400456,0.997462,0.00003771879,0.00003711097,0.001556781],"study_design_scores_gemma":[0.00001937441,0.0003848549,0.08991552,0.0000284746,0.0001212724,0.001224105,0.0001132029,0.001763796,0.9035535,0.0002520717,0.002600417,0.00002332342],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618342,0.005581031,0.02592461,0.000181757,0.0001141592,0.0001400747,0.003642882,0.0001517844,0.002429561],"genre_scores_gemma":[0.9330025,0.003594752,0.05347017,0.0002516996,0.00008947328,0.0004231365,0.005462264,0.00006756504,0.003638571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009754281,"threshold_uncertainty_score":0.003072798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04873772682197217,"score_gpt":0.336513401832002,"score_spread":0.2877756750100298,"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."}}