Fruska gora mountainous environments - assessing the impact of geological setting and land use on soil properties
Bibliographic record
Abstract
On a global scale, it has been found that in the last decades the surface of the vulnerable land and land affected by degradation is increasing and that unsustainable land management is one of the key drivers of land degradation. In order to assess the effect that these changes have on biodiversity and ecosystem services, and to carry out the appropriate planning and management actions for conserving the environment it is essential to identify and quantify changes caused by land degradation. The aim of this study was to determine the impact of geological setting i.e. type of bedrock, and land use on soil physico-chemical properties in vulnerable mountainous areas of Fruskagora. For the purpose of this study the total of 30 soil samples at 0-20 cm depth were collected at four locations on the Fruskagora Mt. Geological setting was serpentinite and marl and land cover was forest and meadow. Following soil properties were determined: pH, redox potential (Eh), electrical conductivity (EC), total dissolved solids (TDS), concentrations of available cations Ca, Mg, K, Na, contents of organic carbon (Corg) and nitrogen (N). The correlation between the obtained parameters was tested with two-way ANOVA and Principal Component Analyses (PCA). All of the obtained results indicate that the soil physico-chemical properties depend on geological setting and that rock composition has to be taken into consideration during land management.
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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 | 0.001 | 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".