Exploring the world beneath your feet : soil mesofauna as potential biological indicators of success in reclaimed soils
Bibliographic record
Abstract
Soil formation is crucial for successful reclamation of industrial affected land. Companies are anxious to obtain ecological data indicating success of their remediation efforts. Soil fauna are a vital part of soil ecosystem function, actively involved in decomposition, nutrient cycling and soil formation. Soil mesofauna are an abundant and species-rich group of organisms in soil that may also provide a useful function as biological indicators of habitat disturbance, soil quality and reclamation success. The primary objective of this study was to compare soil mesofauna communities among natural and reclaimed sites and establish baseline data to allow for long-term monitoring of recolonization on disturbed sites. Reclamation prescription significantly influenced density and community structure of soil mesofauna. Densities were greater in natural soils than in reclaimed soils and community structure differed between natural and reclaimed soils. Integration of this biological data with other monitored soil properties should provide a better overall indication of soil ecosystem recovery and reclamation success. [All papers were considered for technical and language appropriateness by the organizing committee.]
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".