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Record W2808354541 · doi:10.1002/ecs2.2289

Soil homogenization and microedges: perspectives on soil‐based drivers of plant diversity and ecosystem processes

2018· article· en· W2808354541 on OpenAlexafffund
Holly J. Stover, Hugh A. L. Henry

Bibliographic record

VenueEcosphere · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHomogenization (climate)EcosystemEnvironmental scienceEcologyEcosystem engineerPatch dynamicsBeta diversityBiogeochemical cycleSoil waterBiodiversitySoil scienceBiology

Abstract

fetched live from OpenAlex

Abstract Disturbance caused by agriculture and resource extraction has resulted in widespread homogenization of soils at the local (within‐site) scale. Here, we describe how experimental manipulation of heterogeneity at the local scale has had inconsistent effects on plant species diversity. Moreover, we discuss how soil homogenization per se typically has not been accounted for in the study of heterogeneity–diversity relationships, and how disturbance, often viewed as an artifact in soil heterogeneity experiments, can be a key driver of soil homogenization. We propose a conceptual model for describing the relationship between plant size, patch size, and diversity, and we discuss how factors such as disturbance, productivity, and competition among species either should be controlled or accounted for in soil heterogeneity–plant diversity experiments. Finally, we consider the concept of soil patch microedges, which may provide unique microsites for increased plant diversity, and how these biogeochemical and hydrological interfaces could potentially drive ecosystem processes in a manner unique from the adjacent patches. Overall, this synthesis integrates perspectives on the functional links between plant‐driven processes, and soil patterns and processes.

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 score0.011
Threshold uncertainty score0.421

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 head0.006
GPT teacher head0.187
Teacher spread0.180 · 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

Citations21
Published2018
Admission routes2
Has abstractyes

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