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Record W2154572052 · doi:10.1139/cjb-2014-0063

Consequences for ectomycorrhizal fungi of the selective loss or gain of pine across landscapes

2014· article· en· W2154572052 on OpenAlexaffvenue
Justine Karst, Morgan Randall, Catherine A. Gehring

Bibliographic record

VenueBotany · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversity of GuelphUniversity of Alberta
Fundersnot available
KeywordsBiologyAbundance (ecology)ObligateEcologyContext (archaeology)EcosystemPopulationSymbiosisBotany

Abstract

fetched live from OpenAlex

Altered disturbance regimes and novel introductions are causing rapid shifts in the distribution of pines (Pinus L.). The functionally obligate symbiosis between pines and ectomycorrhizal (EM) fungi dictates that shifts in the distribution of one partner will affect the distribution of the other. In this review, we examine evidence for three hypotheses. (1) The loss of photosynthates through stress or tree mortality decreases the abundance of EM fungi and selects for less carbon-demanding species. (2) Pine introductions initiate establishment of novel EM fungal communities. (3) The extent of shifts in EM fungal abundance and community composition is mediated by surrounding vegetation. We find support for these hypotheses though changes in EM fungal abundance are variable and context-dependent. We posit that the consequences of shifts in EM fungal abundance and community composition extend beyond the individual tree to the landscape; these changes may affect population dynamics of both symbionts, ecosystem processes, and the conservation and evolution of fungi. In addition to conducting baseline surveys to assess the distribution of EM fungi, increasing our understanding of their function, morphology, propagation, and controls on host-specificity, and shifts would also assist in predicting the trajectory of ecosystems following the loss or gain of pine.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.716

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)0.0010.000

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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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 designBench or experimental
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

Citations31
Published2014
Admission routes2
Has abstractyes

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