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Record W2947862083 · doi:10.1111/rec.12990

Restoration of belowground fungal communities in reclaimed landscapes of the Canadian boreal forest

2019· article· en· W2947862083 on OpenAlexafffundabout
Gregory J. Pec, Natalie Scott, Stefan F. Hupperts, Shanon L. Hankin, Simon M. Landhäusser, Justine Karst

Bibliographic record

VenueRestoration Ecology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaGovernment of Alberta
KeywordsDisturbance (geology)TaigaEcosystemEcological successionEcologyBorealEnvironmental sciencePlant communityAgroforestryClearcuttingForest ecologyBiology

Abstract

fetched live from OpenAlex

The trajectory of forests establishing on reclaimed oil sands mines in the Canadian boreal forest is uncertain. Soil microbes, namely mycorrhizal fungi, partly underlie successional trajectories of plant communities, yet their role in restoration is often overlooked. Here, we tested the relative importance of common management tools used in restoration—species planted and soil placement—on the recovery of ectomycorrhizal fungal communities over 4 years. Importantly, we further compared the community assembly of fungi on reclaimed landscapes to that in reference ecosystems disturbed to different degrees. This latter test addresses whether disturbance intensity is more important than common management interventions to restore fungal communities in these ecosystems. Three main findings emerged. (1) The effect of tree species planted and soil placement on ectomycorrhizal fungal communities establishing on reclaimed landscapes was dynamic through time. (2) Disturbances that remove or disrupt the organic layer of soils substantially affect the composition of ectomycorrhizal fungal communities. (3) Shifts in the community composition of ectomycorrhizal fungi were driven to a greater extent by disturbance severity than either tree species planted or soil placement.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.637

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 head0.209
Teacher spread0.195 · 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

Citations7
Published2019
Admission routes3
Has abstractyes

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