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Record W2577618845 · doi:10.1126/science.aai8212

Plant-soil feedbacks and mycorrhizal type influence temperate forest population dynamics

2017· article· en· W2577618845 on OpenAlexafffund
Jonathan Bennett, Hafiz Maherali, Kurt O. Reinhart, Ylva Lekberg, Miranda M. Hart, John N. Klironomos

Bibliographic record

VenueScience · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversity of GuelphUniversity of British Columbia, Okanagan Campus
FundersNatural Sciences and Engineering Research Council of CanadaMax-Planck-Gesellschaft
KeywordsBiologyEcologyTemperate forestSoil biologyTemperate climatePopulationArbuscular mycorrhizalMycorrhizaBiotaEcosystemFacilitationForest ecologyPlant communityTemperate rainforestSymbiosisSoil waterEcological succession

Abstract

fetched live from OpenAlex

Feedback with soil biota is an important determinant of terrestrial plant diversity. However, the factors regulating plant-soil feedback, which varies from positive to negative among plant species, remain uncertain. In a large-scale study involving 55 species and 550 populations of North American trees, the type of mycorrhizal association explained much of the variation in plant-soil feedbacks. In soil collected beneath conspecifics, arbuscular mycorrhizal trees experienced negative feedback, whereas ectomycorrhizal trees displayed positive feedback. Additionally, arbuscular mycorrhizal trees exhibited strong conspecific inhibition at multiple spatial scales, whereas ectomycorrhizal trees exhibited conspecific facilitation locally and less severe conspecific inhibition regionally. These results suggest that mycorrhizal type, through effects on plant-soil feedbacks, could be an important contributor to population regulation and community structure in temperate forests.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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

Citations751
Published2017
Admission routes2
Has abstractyes

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