{"id":"W4394052197","doi":"10.5281/zenodo.3766139","title":"Geometric renormalization unravels self-similarity of the multiscale human connectome","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spaceflight effects on biology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Connectome; Self-similarity; Renormalization; Similarity (geometry); Statistical physics; Functional connectivity; Computer science; Mathematics; Geography; Physics; Artificial intelligence; Psychology; Neuroscience; Geometry; Mathematical physics; Image (mathematics)","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":["insufficient_payload"],"category_scores_codex":[0.0006913729,0.0002880826,0.0005294406,0.0005736722,0.001142761,0.000150357,0.001277729,0.0003522741,0.007471949],"category_scores_gemma":[0.002587018,0.0002346692,0.0001567261,0.001899244,0.0002827134,0.00008154508,0.001828973,0.0008045839,0.00324386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002451161,"about_ca_system_score_gemma":0.0000165125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006713912,"about_ca_topic_score_gemma":0.000001640915,"domain_scores_codex":[0.9971489,0.0007597961,0.0004951434,0.0005879095,0.0006226973,0.0003855658],"domain_scores_gemma":[0.9973732,0.00007343935,0.0004471919,0.001074403,0.0007998893,0.0002318362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007524962,0.0002608259,0.00001475841,0.0006264338,0.0001632395,0.00002108631,0.0001258539,0.000003568858,0.002926746,0.0001060072,0.9948325,0.000843687],"study_design_scores_gemma":[0.001147498,0.0007517255,0.002477385,0.0001174428,0.0002011053,0.0001571362,0.00003066798,0.00004333108,0.00190227,0.00001299884,0.9929456,0.0002128938],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001850911,0.0001234767,0.0002887686,0.001224344,0.000290174,0.001985618,0.988929,0.0006409106,0.004666833],"genre_scores_gemma":[0.02101891,0.0001996157,0.00008626561,0.0004477627,0.0002731251,1.075368e-7,0.9768764,0.0009073559,0.0001904693],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.019168,"threshold_uncertainty_score":0.9975322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03621245679705588,"score_gpt":0.2784422408706853,"score_spread":0.2422297840736294,"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."}}