Ecological evaluation of natural water bodies in the south part of Noginscky Region Moscow District
Bibliographic record
Abstract
Abstract Floristic composition of terrestrial and aquatic plants in the ecotopes of surface water bodies, Blue Lakes and Lake Biserovo, was studied to assess environmental health of aquatic ecosystems in the Moscow region. The research was carried out both in the field and in the laboratory. Blue Lakes were lacking aquatic vegetation and the dominant synusia of the following plant species was found along the coastal zone: meadow clover ( Trifolium pratense L.), meadow bluegrass ( Poa pratensis L.), sedge coastal ( Carex riparia Curt.), chamomile ( Matricaria chamomilla L.), plantain ( Plantago major L.), Timothy meadow ( Phleum pratense L. ). The vegetation cover of Lake Biserovo was patchy: submerged vegetation was most widespread, Canadian Elodea ( Elodea canadensis Michx.) was the dominant species. Pure thickets of vegetation were formed only by the common American water plantain( Alisma plantago-aquatica L.), lake cattail ( Schoenoplectus lacustris L.), common reed ( Phragmites communis Trin.) and sedge coastal ( Carex riparia Curt). It should be noted that in all the studied water bodies, the pH of the water area can be classified as very clean: Blue Lakes (7.8), samples from Lake Biserov (8.32). Water samples from Lake Biserova had the highest turbidity (11.5), which was associated with the accumulation of particles of the silt fraction of the reservoir, which began the process of eutrophication, high anthropogenic load. According to the results of a comprehensive assessment (hydrological indicators, calculation of the Mayer index) of the studied samples from the reservoirs, it was concluded that the water area of the Blue Lake had the best environmental status in the Noginsk region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".