{"id":"W4394720749","doi":"10.1152/japplphysiol.00896.2023","title":"A constrained constructive optimization model of branching arteriolar networks in rat skeletal muscle","year":2024,"lang":"en","type":"article","venue":"Journal of Applied Physiology","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Skeletal muscle; Constructive; Computer science; Blood flow; Adaptability; Branching (polymer chemistry); Network model; Anatomy; Artificial intelligence; Chemistry; Biology; Cardiology; Process (computing); Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004628958,0.0006687855,0.0004620038,0.0004688677,0.0002594183,0.0005933011,0.0007167663,0.00108576,0.001614109],"category_scores_gemma":[0.001171873,0.0003227677,0.0005294129,0.0004135068,0.0005639658,0.0003733809,0.0006029909,0.0006113441,0.0002382823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007450262,"about_ca_system_score_gemma":0.001040472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007376746,"about_ca_topic_score_gemma":0.00718751,"domain_scores_codex":[0.9998678,0.00004874829,0.000004019564,0.00003395946,0.00003079056,0.00001463644],"domain_scores_gemma":[0.9995607,0.0002763502,0.00005378512,0.00001640453,0.00006362737,0.00002925501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006565759,0.000005302166,0.00009541546,0.00001465028,0.000003354095,0.00002060735,0.000008662803,0.9930198,0.000624687,0.003865718,0.0001161567,0.00221918],"study_design_scores_gemma":[0.000001175077,0.000004544589,0.00002016488,0.000001971937,0.000001303918,0.000003889744,0.00000164973,0.9988863,0.0001037229,0.0008131174,0.0001609289,0.000001273462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02471661,0.0002678048,0.9673983,0.0001503414,0.00001997752,0.00005673351,0.0001706277,0.0001612623,0.007058294],"genre_scores_gemma":[0.6883594,0.0005643305,0.3020942,0.0001222138,0.00003220305,0.000476044,0.0003654679,0.0001441413,0.007842051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007376746,"threshold_uncertainty_score":0.01466763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00783984070922885,"score_gpt":0.2519751619271364,"score_spread":0.2441353212179076,"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."}}