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Record W4403559307 · doi:10.1016/j.isci.2024.111193

Simulated fire and plant-soil feedback effects on mycorrhizal fungi and invasive plants

2024· article· en· W4403559307 on OpenAlexaff
Kendall E Morman, Hannah L. Buckley, Colleen M. Higgins, Micaela Tosi, Kari E. Dunfield, Nicola J. Day

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsUniversity of Guelph
FundersVictoria University of WellingtonRoyal Society Te ApārangiFaculty of Science, Victoria University of WellingtonVictoria UniversityAuckland University of Technology, New Zealand
KeywordsMycorrhizal fungiEnvironmental scienceArbuscular mycorrhizal fungiSoil fungiPlant sciencePlant physiologyBotanyBiologyEcologyAgronomyHorticultureInoculation

Abstract

fetched live from OpenAlex

<h2>Summary</h2> Climate change intensifies fires, raising questions about their impacts on plant invasions via changes in soil biota and plant-soil feedback (plants alter soil conditions, changing plant growth and vice-versa). We explored effects of plant-soil feedback and simulated fire (heat) on mutualistic arbuscular mycorrhizal (AM) fungal communities and invasive plant growth. Soils were collected from a dominant native grass (<i>Chionochloa macra</i>) and two invasive hawkweeds (<i>Hieracium lepidulum</i>, <i>Pilosella officinarum</i>) in a New Zealand grassland and then heated. In our experiment, both hawkweeds exhibited greater biomass in <i>Pilosella</i> soils, which also had the highest AM fungal richness. Heat had little effect on plant biomass or AM fungal community composition and richness. Hawkweeds altered AM fungal communities relative to the dominant native grass, and moderate soil heating increased <i>Hieracium</i> growth. <i>Hieracium</i> plants also grew better in <i>Pilosella</i> soils, suggesting the potential for soil-mediated invasional meltdown whereby one invasive species facilitates invasion by another.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.173

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 head0.009
GPT teacher head0.204
Teacher spread0.194 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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