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Record W2994682078 · doi:10.1111/rec.13099

Neighborhood effects on soil properties, mycorrhizal attributes, tree growth, and nutrient status in afforested zones

2019· article· en· W2994682078 on OpenAlexaff
Min Sheng, Xuedong Chen, Xiaojuan Yu, Jie Yan, Xinlu Zhang, Chantal Hamel, Yangyang Sheng

Bibliographic record

VenueRestoration Ecology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Natural Science Foundation of China
KeywordsRobiniaAfforestationHippophae rhamnoidesRhizosphereBiologyMonocultureLocustBotanyAgronomyEcology

Abstract

fetched live from OpenAlex

A harmonious interspecies relationship is the key to the success of mixed afforestation. This study was conducted to assess the responses of afforestation species to their neighboring trees. We examined five types of stands—monocultures of Chinese pine ( Pinus tabuliformis ), black locust ( Robinia pseudoacacia ), sea‐buckthorn ( Hippophae rhamnoides ), and two mixtures (Chinese pine × black locust mixture and Chinese pine × sea‐buckthorn mixture)—in the Loess Plateau, northwestern China. The height and diameter at breast height of each tree species were measured, and rhizosphere soil, shoot, and root were sampled. In monocultures, black locust was taller than Chinese pine and sea‐buckthorn, while the height of Chinese pine and sea‐buckthorn was similar. In mixtures, Chinese pine grew better with sea‐buckthorn than alone as a result of modified soil properties and plant nutrition, but not with black locust. When Chinese pine was used as neighbors, it affected the level of arbuscular mycorrhizal (AM) colonization of black locust, soil properties and AM fungal spore density of black locust and sea‐buckthorn, but did not significantly affect their growth. Our results suggest that the reciprocal effects between tree species in mixture are not symmetric, and thus planning for efficient mixed afforestation requires knowledge of species‐specific growth rate, nutrient requirements, and species interactions.

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 score0.008
Threshold uncertainty score0.457

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 head0.012
GPT teacher head0.180
Teacher spread0.168 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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