Assessing nutrient loading from reclaimed water irrigation using the chemical marker iohexol
Bibliographic record
Abstract
Abstract Reclaimed water irrigation is a beneficial practice that could worsen nitrogen impairment of surrounding waterbodies. Estimating this contribution requires development of a suitable chemical marker. Toward that end, reuse effluents throughout Florida analyzed by liquid chromatography tandem mass spectrometry or high‐resolution atomic mass identified the radiographic contrast medium iohexol as a marker unique to reclaimed water. Because iohexol may be subject to biodegradation during subsurface transport or photolability during surficial flow, this study measured iohexol degradation from solar insolation and its stability relative to nitrate during soil transport for inclusion within a mass balance calculation for a nitrogen impaired surface water in Naples, Florida. In this application, the reuse irrigation fractional volumetric flow contribution was ≤7% of the flow to the impaired waterbody. Subsurface flow was not considered because column experiments with local soil demonstrated preferential denitrification over iohexol biodegradation. Additional studies are needed to further demonstrate the potential merit of this approach in different regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".