Bias of stormwater infiltration measurement methods evaluated using numerical experiments
Bibliographic record
Abstract
Abstract Infiltration stormwater control measures (SCMs) have the potential to contribute towards mitigating the effects of urbanization on downstream receiving waters. Infiltration SCMs are most often successful when the in‐situ saturated hydraulic conductivity ( K sat ) is well characterized. In this paper numerical solutions of the Richards’ equation are used to quantify the bias of seven infiltration measurement methods, removing natural variability and random error from the analysis. The methods evaluated in this study include the double ring infiltrometer, Saturo infiltrometer, modified Philip–Dunne infiltrometer, Turf‐Tec IN2‐W infiltrometer, USBR 7300‐89 well permeameter, Philip–Dunne permeameter, and the Guelph permeameter. Seven homogenous, isotropic soil textures were simulated at four initial soil moistures for the seven methods, resulting in a total of 196 simulations. The dimensionless bias is defined as the “measured” K sat determined by a given method divided by the K sat input to the numerical experiments. The “measured” K sat is in quotations to identify the measurement occurs in a numeric experiment rather than in a physical experiment. In sand through silt loam soils that are typical of infiltration SCMs, the simulated methods have a bias in the range of 0.7–6.2. The Turf‐Tec was the only infiltrometer that produced a bias >2.5 for these soils. Initial effective saturation had a minimal contribution to bias for most methods. Methods that rely on a one‐dimensional (1D) flow assumption consistently overestimated the K sat . Borehole methods produced results with bias similar to surface methods. Long duration methods did not consistently produce more accurate results than short duration methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".