What are the principal environmental filters driving species composition and succession on mineralogically different spoil heaps?
Bibliographic record
Abstract
The relationship between selected environmental variables and plant species composition was studied on two mineralogically different spoil heaps (Hg and Cu) in Central Slovakia with contrasting reclamation approaches. Data on plant species composition were collected by stratified random sampling in defined physiognomic vegetation types. A detrended correspondence analysis showed that most of the variability in species composition was related to the succession gradient from open communities with a low cover of vascular plants to forest vegetation, and to the moisture gradient. Variance partitioning by canonical correspondence analysis revealed that most of the variability in plant composition was related to the content of various heavy metals (27.8% at the Hg-spoil heap and 28.3% at the Cu-spoil heap), but a significant relationship was found only for Mn. Other significant factors comprised soil moisture, pH and P content for the Hg-spoil heap and soil temperature and Ca content for the Cu-spoil heap. Although heavy metal content explained most of the variability in species composition, the relationship was caused by the correlation of heavy metal content with other environmental variables rather than by a direct causal relationship.
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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 | 0.001 | 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".