Earthworms and soil mesofauna as early bioindicators for landfill restoration
Bibliographic record
Abstract
Context Landfill activities physically disrupt soil habitats. When restoring landfill to ‘soft’ end uses such as woodland, the appropriate stockpiling and reapplication of native topsoil to provide suitable soil habitats may enhance soil fauna recolonisation and reduce restoration timeframes. Aims We studied the influence of reclamation practices on earthworm and mesofauna communities, and evaluated the role of earthworms and soil mesofauna as bioindicators of early landfill restoration success. Methods We investigated soil physico–chemical parameters, and earthworm and soil mesofauna communities at two restored landfill sites and the surrounding land uses. We also applied the QBS-collembola (QBS-c) and QBS-earthworm (QBS-e) index techniques in a reclaimed landfill setting. Results Natural colonisation of reclaimed landfill by earthworms occurred rapidly where original site topsoil was stockpiled, reapplied, and revegetated. QBS-e and QBS-c indices indicated that the most disturbed sites generally had the lowest soil biological quality. Mesofauna richness and abundance were generally higher in the low-disturbance sites. Conclusions and implications We demonstrate the value of recording a range of soil invertebrates during land reclamation, since different soil bioindicator groups respond differently to soil disturbance. QBS-c and QBS-e index techniques alongside traditional soil macro- and mesofauna assessments reinforced our observed soil fauna responses to reclamation practices. Thus, we encourage multitaxon soil monitoring during land reclamation. Where landfill restoration was carried out to a poor standard, results suggest that soil mesofauna are better indicators of soil status than earthworms. For future restoration schemes, best-practice methods are recommended to improve poor-quality reclaimed soil materials to facilitate rapid soil fauna recolonisation and soil structural development.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".