State of the science and the way forward for the ecotoxicological assessment of contaminated land
Bibliographic record
Abstract
Over the past two decades, soil ecotoxicologists have made strides in utilizing the basic concepts and advancements in soil zoology and ecology. They have applied the existing tools, and developed new ones to investigate how chemical contamination can affect soil ecosystems, including the degradation or destruction of soil quality and habitats or the diminishment of belowground biodiversity. Soil ecotoxicologists are applying a suite of standard protocols, originally developed as laboratory tests with single chemicals (e.g., pesticides), and further enhancing both the approaches and protocols for the assessment of contaminated lands. However, ecological relevance of some approaches remains unresolved. The authors discuss the main challenges for a coherent ecotoxicological assessment of soil ecosystems amid contaminated lands, and provide recommendations on how to integrate the effects of physical and chemical soil properties, the variations in the diversity of soil invertebrates, and the interactions among organisms of various trophic levels. The review examines new international approaches and test methods using examples from three continents (in particular research conducted in Brazil), and provides recommendations for improving ecological relevance of ecotoxicological investigations of contaminated lands.
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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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".