Assessment of spatial and temporal water quality distribution of Lake Ludas, Serbia
Bibliographic record
Abstract
Abstract This work presents the analysis of both spatial and temporal water quality distribution of Lake Ludas in the Republic of Serbia using water quality data from 2011 to 2018 at three different locations. By including a set of standard methods, the initial 15 water quality parameters were reduced to 7 parameters representative for the upcoming temporal and spatial considerations. The selected parameters were subjected to a series of tests such as spatial and temporal analysis. Principal component analysis (PCA) was employed to present the variation of the measurements most efficiently and identify spatial and temporal tendencies. The PCA was expanded by the utilization of biplots providing a more comprehensive understanding of the measurements. Finally, the overall state of the lake's quality was evaluated using the Canadian Council of Ministers of the Environment Water Quality Index method for each sampling location, both annually and for the overall time interval, and as one representative value for the whole lake. The presented research lead to several conclusions, including the need for more detailed future measurements. It was shown that a reasonable monitoring approach leading to reliable conclusions should include much denser data in space and time. Furthermore, the necessity of three sampling locations remains relevant. In fact, it would be preferred to have a shorter list of monitored variables covering denser time and space data acquisition than having more diverse quality parameter evaluation at fever locations or temporally sporadic measurements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".