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Record W2768718556 · doi:10.1080/11956860.2017.1403242

Plant belowground diversity and species segregation by depth in a semi-arid grassland

2017· article· en· W2768718556 on OpenAlexafffundvenue
Zhi Li, Eric G. Lamb, Candace L. Piper, Steven D. Siciliano

Bibliographic record

VenueEcoscience · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsBiologyElymusPlant communitySpecies richnessBromusEcologySpecies evennessRuderal speciesCarexGrasslandFestucaGraminoidSpecies diversityCyperaceaeBromus tectorumBotanyPerennial plantHabitatPoaceae

Abstract

fetched live from OpenAlex

Understanding the relationships between below- and aboveground plant community diversity and composition is essential for understanding plant–soil linkages and feedbacks. Here we examine the patterns of belowground plant species richness, evenness, community composition, and individual species relative abundance with soil depth in a rough fescue grassland. Plant taxa belowground were identified via next-generation sequencing of the trnL intron. We found weak positive below–aboveground concordance in plant species composition demonstrating a general similarity between aboveground and belowground communities. The positive relationship between below- and aboveground plant species richness was stronger within the A horizon than in the B horizon, indicating that some species avoided rooting more deeply in the soil. For the most common graminoid species at the site, there was no evidence for depth-related rooting preferences for species of Carex (sedges) or Elymus (wheatgrasses), while the dominant native grass Festuca hallii preferentially rooted shallowly while the invasive grass Bromus inermis rooted more deeply. Differential rooting patterns among some of the dominant species suggests some degree of belowground niche differentiation may be important in structuring this plant community.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.016
GPT teacher head0.219
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.

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

Citations19
Published2017
Admission routes3
Has abstractyes

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