Spatial and Temporal Analysis of Nitrate Dynamics along the Tigris River
Bibliographic record
Abstract
Given the wide dependency on surface water used to supply drinking water, agricultural irrigation, and industrial activities, nitrate pollution has posed a serious concern in the Tigris River in recent years. The main objective of this study was to develop an understanding of the spatiotemporal patterns of nitrate distribution in the Tigris River through an integrated approach using hydrological data, physicochemical parameters, and model-based analysis. Eighty-four monthly sampling campaigns from forty monitoring locations along the Tigris River were carried out from January 2011 to December 2018. Obtained results demonstrated that the NO3- dynamics were strongly correlated with the length of transport distance and flow rates along the river system (p < 0.05). High flow rates in the upper courses of the river system favored physical transport of NO3- and promoted a dilution effect. However, low flow rates in the lower sections favored the accumulation processes of NO3- and promoted a concentration effect. High concentration of 7.0±1.96 g NO3- m-3 was observed in February 2018 downstream in the river. No significant seasonal effect in NO3- concentrations were observed. These results were supported by the changes in dissolved oxygen concentration and pH in the river system and indicated high nitrification rates and elevated NO3- accumulation, particularly downstream in the river. This modeling approach has also confirmed field observations of NO3- dynamics with 65% of the variances in the river system being explained by the model.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".