Determining Long‐Term (Decadal) Deep Drainage Rate Using Multiple Tracers
Bibliographic record
Abstract
The deep drainage rate is a critical hydrological parameter in understanding contamination mechanisms of soil and groundwater. Little research has been conducted on the temporal variations in deep drainage rate during the last century. The objective of this study was to determine the long-term deep drainage rate on a cultivated loamy soil in the Canadian Prairies. Three tracers were used: KCl applied in 1971, fallout tritium in 1963, and NO3* released during the initial cultivation of the field (1923). Two soil cores to a depth of 3.6 m were taken along a flat portion of the field, and soil Cl(-), 3H, and NO3* concentrations were measured as a function of depth. An additional four cores were taken for soil water content measurements between 2000 and 2003. Distinct peaks in the depth distribution of these three tracers were located at 1.27 m for Cl(-), 1.31 m for 3H, and 1.52 m for NO3*, 32, 40, and 80 yr after the application of Cl(-), 3H, and NO3*, respectively. The average deep drainage rates, calculated as the product of the estimated tracer velocity and volumetric soil water content below the active root zone, were 2.0 mm yr(-1) from the Cl(-) tracer, 2.2 mm yr(-1) from 3H, and 2.5 mm yr(-1) from the NO3* tracer. Therefore, there was little temporal variability in the groundwater recharge over the eight decades that the field has been cultivated. The recharge rates are less than 1% of the mean annual precipitation (333 mm).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".