Evaluation of Cr in ophiolite and groundwater and its potential to contaminate the environment in SE of Birjand
Bibliographic record
Abstract
The presence of Cr(VI) in groundwater resources is governed by pH and Eh of water and its compounds are generally soluble and have more toxicicity and mobility in oxidizing environments. In this article, the Cr concentration in ophiolite units, in sediments, and in groundwater resources, and also its potential to contaminate the environment have been investigated in southeast of Birjand. During sampling, 17 water samples (2 rain water samples and 15 groundwater samples), and 8 sediment samples were collected. The concentrations of cations (major cations and Cr) and anions in water samples were measured at Ottawa University, Canada using IC and ICP-AES methods, respectively. Cr concentrations of sediments were measured using XRF, and concentrations of Cr in collected Selective Sequential Extraction (SSE) fractions were measured using Atomic Absorption (AA) at Ferdowsi University of Mashhad, Iran. The average Cr concentrations in sediments and water resources are 627 and 0.026 ppm, respectively. According to the pH of sediments and Eh-pH of water samples, the Cr in water resources is as Cr(VI). Furthermore, the results of SSE show that the majority of Cr was found with residual matter, attached to the iron and manganese oxides, bound to carbonates, organic matter, and the soluble fractions, respectively. The hydrogeochemical properties of water resources show that the average values of EC, TDS and pH are 509 mg/l, 1045 µs/cm and 8.1, respectively, and the concentrations of Cl-, Na+, Mg2+ and SO42- ions are higher than the levels of WHO and Iran National Standard (1053). According to the WQI classification, while 20 percent of the water resources have excellent quality, 53 percent show good quality and 20 percent of water resources are poor in quality.
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 | 0.001 |
| Science and technology studies | 0.001 | 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".