Temporal monitoring of free-living nematode communities for evaluation of soil health in an arable crop rotation
Bibliographic record
Abstract
There is a growing interest in finding reliable methods for monitoring soil health using bioindicators. Free-living nematodes are an ideal indicator group because of their rapid response to changes in soil conditions. This UK study aims to assess their efficacy as bioindicators using two field experiments. In Experiment-1, the treatments included Farmyard Manure, Green Manure consisting of a mix of Raphanus sativus and Vicia sp., and Standard Practice serving as the control receiving N-fertiliser only. The same treatments, except Farmyard Manure, were compared in Experiment-2, which was on a sloping site with a different textured soil. Soil samples were collected twice during each crop season, in Spring and Autumn, for Experiment-1, and only in Autumn for Experiment-2. Ecological indices that categorise nematodes by feeding preference using morphological differences and life strategies (i.e. functional guilds) were calculated. Indices were compared with the abundance of nematode trophic groups to evaluate their use as soil indicators for understanding crop management practices and their legacy effects. Results showed that identification to trophic groups alone was not a sufficiently sensitive approach for assessing changes in the selected management practices. The variations among trophic groups and treatments within the same sampling period were significantly different for bacterivores, fungivores, predators, omnivores, and herbivores. These differences did not always cooccur within the same sampling period, with bacterivores and plant-parasites of economic importance showing greater responses. The food web analyses, calculated by applying the Enrichment Index and Structure Index, and Plant Parasite Index, provided a more sensitive indicator and allowed more effective diachronic monitoring. While using the composition of trophic groups appears to be an attractive solution, their application is best linked to quantifying short-term changes in soil condition and were not as well suited to longer-term soil health monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".