Crop tree release increased the density of soil nematodes and improved the food web structure
Bibliographic record
Abstract
As a special thinning method, crop tree release (CTR) has a beneficial effect on forest environments and structures by changing forest light, heat, and water. However, the impact of CTR on underground biodiversity remains unclear. Therefore, we analyzed the composition, diversity, and metabolic footprints of soil nematode communities under three CTR (100, 150, and 200 trees·ha–1) treatments, as well as a no CTR treatment, in Pinus massoniana Lamb. plantations. The results showed that CTR increased the density of soil nematodes (P < 0.05), the number of omnivore–predator nematodes (P < 0.05), and the diversity (H′) of nematodes (P < 0.05) and enriched the food web structure of soil nematodes. In the medium CTR density treatment (150 trees·ha–1), the nematode density and diversity (H′) were the highest (P < 0.05), the number of omnivore–predator nematodes was also the highest (P < 0.05), and the enrichment index and structure index values of the soil nematodes reached the maximum at the depth of 0–10 cm (P < 0.05). Our results indicated that the community structure of soil nematodes became more stable and mature after CTR, which may be attributed to the changes of soil condition, especially soil organic matter, and plant diversity indirectly.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".