Nitrification and nitrogen mineralization in agricultural soils contaminated by copper mining activities in Central Chile
Bibliographic record
Abstract
Microbiological bioassays of nitrification and nitrogen mineralization have been used for evaluation of soil quality on metal-contaminated soils.We evaluated the effectiveness of nitrification and nitrogen mineralization bioassays as quality indicators of soil degradation caused by metal contamination.We performed standard tests based on protocols of ISO 14238 (2012) and ISO 15685 (2012) on 90 soil samples collected from agricultural areas in central Chile that were historically contaminated by mining activities.Potential nitrification rate (PNR) was best explained by pH and organic matter content (OM) (R 2 =0.32), while nitrogen mineralization (Nmin) was best explained by OM and clay content (R 2 =0.44).Following normalization of the bioassays responses with respect to OM yielded significant correlations between PNR and pH and total Cu content (R 2 =0.22), and between Nmin and clay and total Cu contents (R 2 =0.19).However, inasmuch as total Cu content improved the regression model showing the inhibitory effect of Cu in both bioassays, it accounted for a mere small proportion of the variance.This was despite the wide range of Cu contents in the soils studied (51-2878 mg kg -1 ).Hence, due to the known sensitivity of the nitrification and nitrogen mineralization process to physicochemical characteristics of soils, these bioassays seem to have limited applicability for metal toxicity assessment in metalcontaminated soils.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".