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Record W2082641117 · doi:10.5194/we-10-44-2010

A systematic review of the recent ecological literature on cushion plants: champions of plant facilitation

2010· review· en· W2082641117 on OpenAlexaff
Anya Reid, Laurent J. Lamarque, Christopher J. Lortie

Bibliographic record

VenueWeb Ecology · 2010
Typereview
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsYork University
Fundersnot available
KeywordsEcologyFacilitationMicroclimateEcosystem engineerShrubCushionEcosystemAridEnvironmental resource managementBiologyGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Abstract. Cushion-forming plant species are found in alpine and polar environments around the world. They modify the microclimate, thereby facilitating other plant species. Similar to the effectiveness of shrubs as a means to study facilitation in arid and semi-arid environments, we explore the potential for cushion plant species to expand the generality of research on this contemporary ecological interaction. A systematic review was conducted to determine the number of publications and citation frequency on relevant ecological topics whilst using shrub literature as a baseline to assess relative importance of cushions as a focal point for future ecological research. Although there are forty times more shrub articles, mean citations per paper is comparable between cushion and shrub literature. Furthermore, the scope of ecological research topics studied using cushions is broad including facilitation, competition, environmental gradients, life history, genetics, reproduction, community, ecosystem and evolution. The preliminary ecological evidence to date also strongly suggests that cushion plants can be keystone species in their ecosystems. Hence, ecological research on net interactions including facilitation and patterns of diversity can be successfully examined using cushion plants, and this is particularly timely given expectations associated with a changing climate in these regions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.463
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.275
Teacher spread0.258 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations80
Published2010
Admission routes1
Has abstractyes

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