AVOIDING A ‘SUPERFICIAL’ UNDERSTANDING OF THE EXCRETION OF WATER; importance of ‘thought’ experiments.
Bibliographic record
Abstract
Our objective is to define how the kidney handles a water load by asking two questions. First, “ What is the actual signal for the release of vasopressin (VP)? ” Our data suggest that the signal is related to the arterial and not the venous plasma Na concentration (P Na ). In more detail, when humans consumed identical water loads after an overnight fast slowly (in 2‐hr) or rapidly (0.5‐hr), there was an equal venous P Na , but a 4‐mM lower arterial P Na 30‐min after rapid water ingestion; only these latter subjects had a large water diuresis (12±1 ml/min). Second, “ How can we estimate of the volume of filtrate delivered the distal nephron to understand what the urine flow rate should be when VP does not act ’? We found a large discrepancy in this estimation using 3 different data sets in rats. First, the (TF/P) Inulin rose 2‐fold between the PCT and the DCT. Second, the medullary interstitial osmolality was ~2500‐mOsm/kg H 2 O in water‐deprived rats. Third, AQP1 was not found in the descending thin limbs (DtL) of superficial nephrons. We conclude that apparent discrepancies in data can be resolved by recognizing heterogeneity of sampling. First, a lower arterial P Na is needed to induce a water diuresis; hence water that was ingested slowly can be retained for future heat dissipation. Second, washout of the medulla can only influence loops of Henle that have water permeability. Although this seems not to apply to superficial nephrons, examining other data such as the (TF/P) Inulin implies that the DtL is permeable to water even if AQP‐1 were truly absent in superficial nephrons. These points indicate that deeper understanding requires integration of all relevant data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.069 |
| Scholarly communication | 0.007 | 0.025 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".