Methods for evaluating human impact on soil microorganisms based on their activity, biomass, and diversity in agricultural soils
Bibliographic record
Abstract
Abstract The present review is focused on microbiological methods used in agricultural soils accustomed to human disturbance. Recent developments in soil biology are analyzed with the aim of highlighting gaps in knowledge, unsolved research questions, and controversial results. Activity rates (basal respiration, N mineralization) and biomass are used as overall indices for assessing microbial functions in soil and can be supplemented by biomass ratios (C : N, C : P, and C : S) and eco‐physiological ratios (soil organic C : microbial‐biomass C, q CO 2 , q N min ). The community structure can be characterized by functional groups of the soil microbial biomass such as fungi and bacteria, Gram‐negative and Gram‐positive bacteria, or by biotic diversity. Methodological aspects of soil microbial indices are assessed, such as sampling, pretreatment of samples, and conversion factors of data into biomass values. Microbial‐biomass C (µg (g soil) –1 ) can be estimated by multiplying total PLFA (nmol (g soil) –1 ) by the F PLFA ‐factor of 5.8 and DNA (µg (g soil) –1 ) by the F DNA ‐factor of 6.0. In addition, the turnover of the soil microbial biomass is appreciated as a key process for maintaining nutrient cycles in soil. Examples are briefly presented that show the direction of human impact on soil microorganisms by the methods evaluated. These examples are taken from research on organic farming, reduced tillage, de‐intensification of land‐use management, degradation of peatland, slurry application, salinization, heavy‐metal contamination, lignite deposition, pesticide application, antibiotics, TNT, and genetically modified plants.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".