Denitrification in a Deep Basalt Aquifer: Implications for Aquifer Storage and Recovery
Bibliographic record
Abstract
Aquifer storage and recovery (ASR) can provide a means of storing water for irrigation in agricultural areas where water availability is limited. A concern, however, is that the injected water may lead to a degradation of groundwater quality. In many agricultural areas, nitrate is a limiting factor. In the Umatilla Basin in north central Oregon, shallow alluvial groundwater with elevated nitrate-nitrogen of <3 mg/L to >9 mg/L is injected into the Columbia River Basalt Group (CRBG), a transmissive confined aquifer(s) with low natural recharge rates. Once recovery of the injected water begins, however, NO3 -N in the recovered water decreases quickly to <3 mg/L (Eaton et al. 2009), suggesting that NO3 -N may not persist within the CRBG during ASR storage. In contrast to NO3 -N, other constituents in the recovered water show little variation, inconsistent with migration or simple mixing as an explanation of the NO3 -N decrease. Nitrogen isotopic ratios (δ(15) N) increase markedly, ranging from +3.5 to > +50, and correlate inversely with NO3 -N concentrations. This variation occurs in <3 weeks and recovery of <10% of the originally injected volume. TOC is low in the basalt aquifer, averaging <1.5 mg/L, but high in the injected source water, averaging >3.0 mg/L. Similar to nitrate concentrations, TOC drops in the recovered water, consistent with this component contributing to the denitrification of nitrate during storage.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".