Seasonal Variations of Sediment and Water Quality Correlated to Land-Based Pollution Sources in the Middle of the Black Sea Coast, Turkey
Bibliographic record
Abstract
The environmental pollution parameters were analyzed at the surface sediment and water samples collected from the untreated discharge basin of Sinop coasts of the Black Sea. On the sediment samples, redox potential, water content, organic matter content and porosity percentages and pH analyses and on the water samples, DO, salinity, BOD 5 , TSS, T ( ℃ ), pH, conductivity, NO 2 -N, NO 3 -N, silicate and organic matter analyses were measured. Water content (%) and porosity (%) of the surface sediment were found in the ranges of 62.9 to 0.1 and 58.9 % to 0.76, respectively. As the pH values of the sediment samples varied in the ranges of 7.23 to 9.4, its organic matter content varied in the ranges of 2.6 to 0.11 %. Results indicated that, there was a domestic pollution at the sediment layer of some sampling stations and water according to some physico-chemical parameters at class 2-slightly/moderately polluted water, or very polluted class 4 water class. According to the Environment Law-Water Pollution Control Legislation, Land-Based Water Quality Classification, generally, it has polluted water and the origin of this pollution is mainly domestic. The pollution loads of the water body also appeared at where were discharged untreated water, sediment layer and water content, especially with its high organic matter content .
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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) | 0.000 | 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".