{"id":"W4298122954","doi":"10.35119/maio.v4i1.121","title":"From your kidneys to your eyes: lessons from computational kidney models","year":2022,"lang":"en","type":"article","venue":"Modeling and Artificial Intelligence in Ophthalmology","topic":"Circadian rhythm and melatonin","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kidney; Renal function; Function (biology); Mass transport; Cotransporter; Computer science; Membrane; Chemistry; Medicine; Biochemical engineering; Internal medicine; Biology; Cell biology; Biochemistry; Engineering; Sodium","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":[0.0009391861,0.0007739563,0.001222132,0.0005528031,0.0005056076,0.001822729,0.001804002,0.001752037,0.002170967],"category_scores_gemma":[0.003902026,0.0005103221,0.001328665,0.0004611267,0.002306019,0.003031021,0.001696315,0.003126615,0.0005582271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001017974,"about_ca_system_score_gemma":0.00146033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006060718,"about_ca_topic_score_gemma":0.004127391,"domain_scores_codex":[0.9997572,0.0001043357,0.00001871005,0.00003815122,0.00006227063,0.00001939199],"domain_scores_gemma":[0.9983316,0.001215677,0.00005649488,0.0001108892,0.0001719237,0.0001133427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003419152,0.00003106305,0.001452729,0.0005149514,0.00010771,0.0002238397,0.0004591969,0.3696573,0.0005781795,0.5795583,0.01172632,0.03565611],"study_design_scores_gemma":[0.00002064767,0.00001834085,0.0003039242,0.0002050489,0.00002356328,0.0001234589,0.0001437033,0.2804986,0.0003028655,0.6931739,0.02514771,0.00003828076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02954798,0.05132892,0.8357772,0.05071953,0.001707787,0.00003967724,0.0005729399,0.0005195799,0.02978636],"genre_scores_gemma":[0.595945,0.1057404,0.2719268,0.006554385,0.002957104,0.0003550382,0.0007621309,0.0006959475,0.0150633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006060718,"threshold_uncertainty_score":0.01205087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.186637329418337,"score_gpt":0.3723630188341871,"score_spread":0.1857256894158501,"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."}}