Water quality assessment of Remeți watercourse, Maramureș, Romania, located in a NATURA 2000 protected area subjected to anthropic pressure
Bibliographic record
Abstract
), dissolved Fe, Mn, water hardness, alkalinity (A) and chloride were measured over the January (I)-October (X) 2021 period. This water course was subjected to anthropic pressure, being polluted with nutrients such as ammonium and orthophosphate ions, iron and manganese. The concentrations of other metals were either low (Al, Ba, Li, Ga, Rb, Ni, Sr, Zn, Cu, Ti) or below the detection limit (Pb, Cd). The study was performed over a period of 8 months, namely January 2021-October 2021, covering the 4 seasons, in order to establish their influence on the level of water quality indicators. Exceeded turbidity values and high concentrations of ammonium, orthophosphate and dissolved iron were found, these being generally higher in the summer-autumn months. Dissolved oxygen concentrations were low in the summer-autumn months. Based on the values of the physico-chemical indicators, two types of water quality indices WA-WQI (weighted arithmetic water quality indices) and CCME-WQI (Canadian Council of Ministers of the Environment water quality indices) were calculated to evaluate the global water quality and its evolution over the seasons with a single value. WA-WQI values varied in the range of 78.56-761.63, with a tendency to increase in autumn, showing an intensified tendency of global water quality deterioration due to an increase in ammonium, turbidity, iron and orthophosphates in autumn months while CCME-WQI values were between 39.6 and 68.9, being fair in winter-spring months and marginal / bad in summer and autumn months. The results of this study are advantageous in identifying the level of pollution of Remeți water course, being a signal for local authorities in taking the necessary measures to reduce the pollution around it, for a better human health and conservation of the ecosystems hosted in the protected area.
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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) | 0.001 | 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".