Interactions among hydraulic conductivity distributions, subsurface topography, and transport thresholds revealed by a multitracer hillslope irrigation experiment
Bibliographic record
Abstract
Abstract Interactions among hydraulic conductivity distributions, subsurface topography, and lateral flow are poorly understood. We applied 407 mm of water and a suite of tracers over 51 h to a 12 by 16.5 m forested hillslope segment to determine interflow thresholds, preferential pathway pore velocities, large‐scale conductivities, the time series of event water fractions, and the fate of dissolved nutrients. The 12% hillslope featured loamy sand A and E horizons overlying a sandy clay loam Bt at 1.25 m average depth. Interflow measured from two drains within an interception trench commenced after 131 and 208 mm of irrigation. Cumulative interflow equaled 49% of applied water. Conservative tracer differences between the collection drains indicated differences in flow paths and storages within the plot. Event water fractions rose steadily throughout irrigation, peaking at 50% sixteen h after irrigation ceased. Data implied that tightly held water exchanged with event water throughout the experiment and a substantial portion of preevent water was released from the argillic layer. Surface‐applied dye tracers bypassed the matrix, with peak concentrations measured shortly after flow commencement, indicating preferential network conductivities of 864–2240 mm/h, yet no macropore flow was observed. Near steady‐state flow conditions indicated average conductivities of 460 mm/h and 2.5 mm/h for topsoils and the Bt horizon, respectively. Low ammonium and phosphorus concentrations in the interflow suggested rapid uptake or sorption, while higher nitrate concentrations suggested more conservative transport. These results reveal how hydraulic conductivity variation and subsurface topographic complexity explain otherwise paradoxical solute and flow behaviors.
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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 | 0.000 | 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 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".