To dam, or not to dam? Abolishment of further flooding impedes the natural revegetation processes after long‐term fluvial deposition of copper tailings
Bibliographic record
Abstract
Abstract Long‐term potentials and constraints of nature to spontaneously recover after severe degradation by toxic mine waste, and general principles of mutual modifications of spontaneous vegetation and soils during this process, have remained widely unknown. This study investigates the effect of flooding on natural restoration of a model locality in Eastern Serbia, complexly degraded by 70‐years fluvial deposition of sulfidic copper (Cu) mine tailings in a floodplain along 80 km of the polluted river flow. We analyzed multivariate response of forest vegetation (floristic and structural parameters) together with physical and chemical properties of concomitant soils and sediments. Floods strongly modify the interactions between soil and vegetation. Three very different types of forest vegetation constitute the response of the nature to key soil adverse factors (excessive Cu availability, low nutrients, and low pH); combined with drought, these constraints completely inhibit revegetation. Continual flooding after mine closure, despite the pollution it still brings in, fosters a faster development of highly specialized vegetation and most importantly faster buildup of soil organic matter necessary for phytostabilization of substantial amounts of Cu tailings present in the floodplain. We show that enhanced nutrient fluxes facilitated by natural flooding regime might overrun the constraining effect of deposited Cu also for natural vegetation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".