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Record W3048431788 · doi:10.1139/cjfr-2020-0236

Crop tree release increased the density of soil nematodes and improved the food web structure

2020· article· en· W3048431788 on OpenAlexvenueno aff
Haifeng Yin, Yu Su, Xianwei Li, Chuan Fan, Gang Chen, Feng MaoSong, Size Liu, Maojin Guo, Xiangjun Li, Yuqin Chen, D.-H. Wu

Bibliographic record

VenueCanadian Journal of Forest Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
Fundersnot available
KeywordsPinus massonianaSoil food webBiologyOmnivoreAgronomyDiversity indexBiodiversityCropCommunity structurePredatorSoil waterSoil biologyEcologyBotanySpecies richnessPredation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.247
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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