Study of the relationship between agrochemical indicators of soils and species composition of plants in some sites of the Lena River Delta, Northern Yakutia
Bibliographic record
Abstract
In connection with climate change and the development of natural resources in the Arctic, increasingly relevant is the problem of revealing the response of tundra vegetation to various natural and anthropogenic impacts, as well as the search for indicators for developing a system of monitoring the state of ecosystems in the Far North. The aim of this work was to describe the floristic composition and structure of plant communities, as well as to search for relationships between agrochemical indicators of soils and the species composition of plants in certain key sites of the Lena River Delta. A total of ten key sites of the Lena delta were studied. Agrochemical analysis of the soil samples was performed using standard methods. The content of biogenic and abiogenic elements was measured using an emission spectrometer. The relationship between the soil parameters and floristic composition was studied using cluster analysis. It was shown that the clustering of sites by standard agrochemical indicators was weakly expressed, while in terms of the content of elements in the mineralized samples and in water extracts the sites were grouped into two clusters at the level of 40 %÷60 % of the differences. The clustering of sites by plant species composition was very weak, and it was not possible to identify clusters with high or medium level of similarity, nor was it possible to detect similarity between cluster diagrams constructed by soil parameters and by plant species composition. As a result of the study, it is shown that the species composition of vascular plants weakly correlates with the properties of the soils of the key sites. The individual features identified manifest themselves in the form of trends and no clear patterns of interrelation of vegetation and soil parameters are discerned.
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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 | 0.001 | 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".