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Record W2084541326 · doi:10.1242/jeb.01019

Dogmas and controversies in the handling of nitrogenous wastes: Excretion of nitrogenous wastes in human subjects

2004· review· en· W2084541326 on OpenAlexaff
Kamel S. Kamel, Surinder Cheema‐Dhadli, Mohammad A. Shafiee, Mitchell L. Halperin

Bibliographic record

VenueJournal of Experimental Biology · 2004
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon Transport and Channel Regulation
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsUreaUrineExcretionContext (archaeology)Uric acidAmmoniaChemistryAmmoniumNet acid excretionKidney stonesKidneyPhysiologyBiochemistryEndocrinologyInternal medicineBiologyMedicineOrganic chemistry

Abstract

fetched live from OpenAlex

Two major nitrogenous waste products, urea and ammonium (NH(4)(+)), are produced in humans when proteins are oxidized, and in this manuscript their excretions are examined from two perspectives. First, the specific physiology of each nitrogenous waste is reviewed and the current dogmas summarized. Second, their excretions are considered in the context of integrative physiology, i.e. the need to ensure that the urine composition is appropriate to minimize the risk of kidney stone formation. After the latter analysis, weak links in our understanding of the overall physiology become apparent and a conundrum is defined. The conundrum for the excretion of urea focuses on the fact that urea is not an effective osmole in the medullary-collecting duct when vasopressin acts. As a result, it appears that urinary urea cannot prevent a large decline in the urine flow rate and thereby minimize the risk of forming kidney stones in electrolyte-poor urine. The conundrum for the excretion of NH(4)(+) is: high rates of NH(4)(+) excretion require a low urine pH, yet a pH approximately 6.0 must be maintained in order to reduce the risk of precipitating uric acid in the urine. Possible ways of resolving these conundrums require novel physiological interpretations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.199
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.311
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2004
Admission routes1
Has abstractyes

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