Land cover transformation in two post-mining landscapes subjected to different ages of reclamation since dumping of spoils
Bibliographic record
Abstract
Transformation of natural land cover (LC) into modified LC has become inevitable due to growing human needs. Nevertheless, landscape transformational patterns during reclamation of mine damaged lands remain vague. Our hypothesis was that post-mining landscapes with different ages since dumping become more diverse in LC transformation over time. The aim was to study the impact of landscape reclamation on land cover changes (LCC) in two post-mining landscapes. Land cover maps of 1988, 1991, 1995, 1998, 2000 and 2003 were produced from LANDSAT TM images of Schlabendorf Nord and Schlabendorf Süd and used to survey the changing landscape. Change detection extension was used to identify changes among land cover types (LCTs). Detrended correspondence analyses (DCA) ordination technique (CANOCO) aided study of similarity among LC distribution. Soil pH analysis was carried out to study effect of soil and climate conditions on LCC. The results show that visible patterns of increase and decrease in the LCTs occurred in both landscapes. Given two post-mining landscapes subjected to different ages of reclamation, clear differences in vegetation growth and LCC pattern would occur. At early stages of restoration, LCTs often have unstable conditions and experience more acute transformation depending on the level of land use intensity in space and time. LCCs were mostly due to progressive and reversed succession. Due to variation in post-mining landscape soil conditions, soil treatment during reclamation should be site specific. The comparative analysis of LCCs in Schlabendorf provides a framework for prioritizing land use planning options for sustainable management of post-mining landscapes in temperate ecosystems.
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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 | 0.000 | 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".