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Community structure of ectomycorrhizal fungi across an alpine/subalpine ecotone

2001· article· en· W2112007592 on OpenAlexfundaboutno aff
Gavin Kernaghan, Karen A. Harper

Bibliographic record

VenueEcography · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEdaphicEcotoneEcologySubalpine forestBiologySpecies richnessAbundance (ecology)Montane ecologyAlpine climateBiodiversitySpecies diversityHabitatSoil water

Abstract

fetched live from OpenAlex

In order to assess changes in the community structure of ectomycorrhizal fungi across the tree line, data on distributions of fungi and their host plants, as well as on edaphic factors and stand age, were collected at two montane sites in the Front Range of the Canadian Rockies. Canonical correspondence analysis (CCA) was used to explore relationships between fungal species composition and environmental factors. Richness and diversity of ectomycorrhizal fungi decreased with elevation, in spite of the fact that host plant diversity was highest at the ecotone between the subalpine forest and the alpine zone. Both host plant distribution and edaphic factors were important in explaining the observed changes in fungal species diversity and composition. The majority of ectomycorrhizal fungi found in the subalpine forest and at the ecotone were conifer associates, while a large proportion of those in the alpine zone were non‐host specific and able to form mycorrhizae with both angiosperms and gymnosperms. The abundance of non‐host specific fungi in the alpine zone is expected to provide a favorable environment for the establishment of conifer seedlings above the present tree line.

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)0.000
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 score0.163
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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

Citations130
Published2001
Admission routes2
Has abstractyes

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