Determining the source and mechanism of river salinity: An integrated regional study
Bibliographic record
Abstract
Zohreh River Basin, Southwest Iran The salinity of Zohreh River sharply increases in three salinity zones (SZs) along the river named SZ1, SZ2 (the focus of this study), and SZ3. Determining the salinity sources and salinization mechanism using an integrated approach including geological, hydrochemical, isotopic, geophysical, river sinuosity and hydrocarbon analysis are the main objectives of this study. The study focuses on the combination of evidence of regional-scale (i.e., river sinuosity and seismic data) and small-scale (i.e., drilling core analysis). Among several known sources of river salinity, it was found that the water quality of the Zohreh River is mainly threatened by the salt-bearing Gachsaran Formation and oil-field brine. It is concluded that halite brine and oil-field brine simultaneously cause the salinization in SZ2, and their contributions were delineated to be 95% and 5%, respectively. The lack of reliable geological evidence to support halite dissolution in surficial layers by circulating waters suggests the possibility of a deep source of halite brine in SZ2. The results revealed that deep halite brine of the salt layers of Gachsaran Formation is mainly responsible for the salinization of SZ2. The mechanism of deep brine penetration to the river through the hidden fault failures detected by the combination of river sinuosity analysis and geophysical data for the first time.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".