Evaluation of environmental impact and risk in Prut River using five indicators (CBO5, CCO-Cr, SO42-, NO2-, NO3-)
Bibliographic record
Abstract
prut River is very important for sustaining the biodiversity and economical activities in its area for Romania, Ukraine and Republic of Moldova. Anthropogenic activities are present everywhere especially near a big water flow because they need a close water resource. The major problems remain the impact and a possible risk produced as a negative result in these activities (agriculture close to river, wastewater from the cities, animal farms, industry and others). In case of Prut River, there were constructed matrices for evaluation of environmental impact and risk using five different indicators (CBO5, CCO-Cr, SO42-, NO2- and NO3-) during different months (September, October, November and December 2013). The sampling sites were fixed between Costesti-Stanca and Giurgiulesti that covered a large area of study. The impacts and risks were analyzed according to each indicator and as total ones. The resulted values were integrated in different classes for impact - EI (B-environmental impact which does not cross the maximum admitted value C-environmental impact that creates slight disturbance of the organisms D-environmental impact that creates high disturbance of the organisms) and risk – ER (A-insignificant risks B-minor risks = monitoring it is not required C-medium risks = monitoring is very required). The conclusions suggested that the impact is different according to each analyzed indicator and to sampling period. The highest peak was recorded between the sampling stations Leuseni and Leova
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".