The Spring to Coast Approach in Small-Scale Catchments and Adjacent Coastal Zone
Bibliographic record
Abstract
Surface waters, particularly rivers, are paramount in serving as the primary global water source and a pivotal economic driver. Various pollution sources can negatively impact water quality. The Water Framework Directive has established regulations that define specific chemical and ecological statuses for rivers. Consequently, there is an ongoing commitment to monitor their quality closely. This study involved the collection of samples from two watersheds (Laspias and Lissos) within the Eastern Macedonia and Thrace Region. The two rivers flow along regions characterized by notable environmental stressors, including WWTP, landfills, industrial zones, and agricultural areas, which also constitute substantial contributors to the local economy. This study’s outcomes, covering from springs to coast, are presented and analyzed using various indices, including the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), discriminate analysis, and the trophic status index (TRIX). Water quality assessment included the measurement of physicochemical parameters, common pollutants, and major ions. The analysis revealed “bad” water quality status along most of the Laspias and in specific sectors of the Lissos, with parameters exceeding the thresholds set by legislation. The rivers demonstrated significant organic and nutrient pollution. Given that water quality in these rivers is significantly influenced by urban, agricultural, and industrial runoff, the imperative need for change necessitates interventions to improve water quality. Observations and measurements are fundamental prerequisites for raising awareness among citizens and stakeholders and for finding effective management measures for the two river basins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".