{"id":"W2550772441","doi":"10.1101/086066","title":"Cracking the Barcodes of Fullerene-Like Cortical Microcolumns","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Biological neural network; Computer science; Neuroscience; Topology (electrical circuits); Spiking neural network; Electronic circuit; Robustness (evolution); Artificial neural network; Cortical neurons; Biological system; Physics; Biology; Artificial intelligence; Mathematics; Combinatorics","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":[],"consensus_categories":[],"category_scores_codex":[0.00007568763,0.0001732008,0.0002273601,0.000368117,0.000478086,0.0007604267,0.000453217,0.0005730387,0.002601601],"category_scores_gemma":[0.0006497593,0.0002044685,0.0001780822,0.0001887534,0.0008183058,0.000616555,0.0006158899,0.0004830086,0.0004347726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004999874,"about_ca_system_score_gemma":0.0002717294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001726553,"about_ca_topic_score_gemma":0.001735577,"domain_scores_codex":[0.9998933,0.000008019481,0.000004849628,0.00002266709,0.00003705031,0.00003403802],"domain_scores_gemma":[0.9997097,0.00006774806,0.00005312405,0.00005412546,0.00005189531,0.00006341391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003105617,0.00007206766,0.00182156,0.0002146117,0.00004171943,0.001095515,0.0003140077,0.04755146,0.8088319,0.1151056,0.002405853,0.02223522],"study_design_scores_gemma":[0.00007654689,0.000254013,0.005150444,0.00006733744,0.00003073023,0.0008141523,0.0003467704,0.267023,0.6620926,0.04598481,0.01804818,0.0001114689],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9565187,0.000271673,0.03562899,0.0003404042,0.0001294737,0.00002424006,0.0002143817,0.0005299983,0.006342157],"genre_scores_gemma":[0.988865,0.00008821825,0.008320971,0.00005797468,0.00001055481,0.00001594111,0.0001032698,0.0000471629,0.00249101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002601601,"threshold_uncertainty_score":0.008703172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01393631828901995,"score_gpt":0.2169567451477555,"score_spread":0.2030204268587356,"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."}}