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Record W4254030193 · doi:10.1007/978-1-60761-219-3_49

Diuretics

2011· book-chapter· ru· W4254030193 on OpenAlexaff
M. Gabriel Khan

Bibliographic record

VenueHumana Press eBooks · 2011
Typebook-chapter
Languageru
Field
Topic
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The action of diuretics on the kidney causes elimination of water along with some electrolytes , particularly sodium and potassium. Figure 1 is a diagrammatic representation of the nephron. The fluid and electrolyte balance of the body is maintained in a constant state by the automatic electronic filtration system provided by the kidney. Each human kidney contains approximately one million nephrons. Each nephron is a mini filter that includes a glomerulus that contains a group of capillary blood vessels. Diuretics. Fig. 1 The nephron and the site of action of diuretics. 1 – single nephron; 2 – the kidney: one million nephrons; 3 – distal tubule site of action of aldosterone, spironolactone, and eplerenone; 4 – amiloride and triamterene; 5 – efferent arteriole; 6 – macula densa; 7 – glomerular capillaries; 8 – afferent arteriole; 9 – thiazides; 10 – loop diuretics, distal tubule; 11 – ascending limb; 12 – loop of Henle; 13 – collecting tubule; 14 – to renal pelvis; 15 – to...

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.110
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1100.068

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 head0.177
GPT teacher head0.275
Teacher spread0.098 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2011
Admission routes1
Has abstractno

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