Empirical model for predicting a catchment-scale metric of surface water transit time in streams
Bibliographic record
Abstract
Estimates of average water velocity (vw) extracted from tracer dye studies (vdye) or calculated from velocitydischarge relationships at continuous-flow gauges (vgage) were combined with catchment area (A) and other readily available data for 111 streams throughout the conterminous United States. The resulting data set (n = 305) represented broad ranges of A (65 62 419 km2), mainstem length (Lmax, 15.6867 km), slope (S, 0.1411.5 m·km1), and daily average discharge (Q, 0.09634 m3·s1). A catchment-scale metric of surface water transit time (Tw, Lmaxvdye1) ranged from 0.3 to 40 days, averaging 7.2 days. A bivariate regression model using log10 A and log10 Q explained 83% of the variation in log10 Tw and predicted Tw with an average precision of ±49%. By contrast, a previously published model based on hydraulic geometry relationships overestimated Tw by 100%. Application of my model to five streams nested in a ninth-order (ω = 9) catchment indicated that under dry (September) and wet (March), long-term (19542001) median flow conditions, vw increased with Q (vw ∝ Q0.3) as far downstream as ω = 8 and then remained constant or declined. The slope of this longitudinal vwQ relationship was three times greater than the expected value. Longitudinal velocity gradients in many streams may thus be much steeper than commonly assumed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".