{"id":"W2552320392","doi":"10.15252/msb.20167216","title":"Dynamical compensation in physiological circuits","year":2016,"lang":"en","type":"article","venue":"Molecular Systems Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation","keywords":"Biology; Compensation (psychology); Computational biology; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002635542,0.0001900248,0.0003004583,0.00009220877,0.00003121991,0.000009435636,0.0002347302,0.0003544721,0.00001555739],"category_scores_gemma":[0.00007900553,0.0001317523,0.0001280702,0.0001517373,0.000133069,0.000002221057,0.0001097938,0.00006147807,0.0000509625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004695763,"about_ca_system_score_gemma":0.00003590215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002669051,"about_ca_topic_score_gemma":0.00003000088,"domain_scores_codex":[0.9981844,0.0004604955,0.0003418163,0.000558832,0.00008577367,0.0003687285],"domain_scores_gemma":[0.9992849,0.00001576894,0.0001033078,0.0004536808,0.00006274209,0.00007959156],"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":[0.00001241285,0.00003348612,0.01433295,0.000005802102,0.00005340932,0.00001036165,0.000003259457,0.0003471317,0.9814467,0.001699964,0.0001458883,0.001908588],"study_design_scores_gemma":[0.01101124,0.003450297,0.3414887,0.0003818954,0.0002287902,0.0004939426,0.0001740755,0.01198046,0.5540133,0.003832289,0.06865945,0.004285585],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9636166,0.001020266,0.03446269,0.0001541516,0.0002153939,0.0002017847,0.000009762496,0.00001996814,0.0002993766],"genre_scores_gemma":[0.9993034,0.00004710329,0.00005563978,0.0001063579,0.0001551967,0.00005089249,0.0001030257,0.00002008144,0.0001582897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4274334,"threshold_uncertainty_score":0.5372702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133547254958493,"score_gpt":0.2391861055719336,"score_spread":0.2278506330223487,"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."}}