Diversity of Nematodes and Nematode Destroying Fungi as Influenced by Land Use in Taita Taveta, Kenya
Bibliographic record
Abstract
Land intensification is thought to lead to soil degradation and consequently to loss of soil biodiversity. A study was undertaken to assess the effect of land use on nematode community and nematode destroying fungi in Taita districts, Kenya. Soil samples were collected from land under various uses which were natural forest, plantation forest, tea, coffee, napier grass, fallow, maize/beans intercrop and horticultural crops production. Chemical analysis of the soil properties was done to determine the amount of carbon, nitrogen and potassium in every land use system. The level of carbon declined with land-use intensification with the highest organic carbon level of 7.6% being recorded in soils from the forest while land with high disturbance recorded 1.6% carbon. Similarly, the intensively cultivated soils had lower nitrogen and phosphorous levels compared to the forest. Nematode and nematode destroying fungi were isolated from the soil and identified. Eighty five isolates, distributed in eight genera and fourteen taxa of nematode destroying fungi were identified. The frequency of isolating nematode destroying fungi increased with increased in land use intensity. All the sampled land use types were significantly (P-value = 3.81x10-07) different in occurrence of nematode destroying fungi. The ratios of free-living to plant parasitic nematodes were 5.18 and 0.54 in the natural forest and annual crop production systems, respectively. The frequency of isolation of nematode destroying fungi was positively correlated to abundance of plant parasitic nematodes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".