Metabolic quotient and specific enzymatic activity in response to the addition of organic amendments to mining tailings
Bibliographic record
Abstract
Abstract Adding organic amendments to mining tailings to ameliorate extreme conditions that limit plant growth is a common practice in reclamation projects; still, the impact on microbial activity is not commonly considered. This work aimed to explore the use of the metabolic quotient and specific enzymatic activity as indicators of microbial carbon use efficiency in response to adding organic amendments to mining tailings. An experiment in vitro on adding organic amendments: compost, biochar, a mixture of them, and no addition on mining tailing from Taxco, Guerrero, Mexico, was established. Carbon mineralization, microbial biomass, and the enzymatic activity of β-glucosidase, phosphatase, polyphenol oxidase, and dehydrogenase were measured, while specific enzymatic activity and metabolic quotient were calculated. The results showed that microbial activity increased by adding all organic amendments in the following order: compost > mixture > biochar. In the treatment with the addition of compost, we observed a higher carbon mineralization and a greater enzymatic activity. The treatment with adding biochar showed similarities with the control treatment in parameters related to carbon dynamics, such as β-glucosidase, dehydrogenase, and carbon mineralization. This reflects microorganisms’ trade-off between investing energy in searching for resources or using them to improve their biomass clearly to view the specific enzymatic activity and metabolic quotient indicators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".