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Record W6892238997 · doi:10.5061/dryad.02v6wwq2w

Data from: Prolonged impacts of past agriculture and ungulate overabundance on soil fungal communities in restored forests

2021· dataset· en· W6892238997 on OpenAlexaff

Bibliographic record

VenueOpen MIND · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
FundersJapan Society for the Promotion of Science
KeywordsExclosureNucleofectionHyporeflexiaFusible alloyTSG101Articular cartilage damage

Abstract

fetched live from OpenAlex

Soil fungi can help improve ecosystem restoration, yet our understanding of how they reassemble in degraded land is limited. Here, using DNA metabarcoding, we studied the fungal community structure in reforested sites following agricultural abandonment and ungulate overabundance. Two treatments, namely ‘reforestation using different numbers of tree species’ and ‘deer exclusion,’ have been applied for multiple decades in the study sites. We found that local fungal richness (alpha diversity) and total fungal richness (gamma diversity) were 1.9–2.9 and 1.3–1.9 times greater, respectively, in reforested stands than in natural forests. These results were regardless of the number of tree species planted in the reforested stands. Conversely, reforested stands had a spatially homogenized community structure with relatively lower degrees of compositional dissimilarity among sites within each stand (beta diversity). These findings were attributable to lower environmental heterogeneity, stronger dispersal limitation, and a comparatively shorter time since the onset of community assembly in reforested stands. Deer exclosures had no detectable effect on fungal community structure. Overall, the agricultural legacy in fungal community structure appears to have persisted for decades, even under proactive restoration of aboveground vegetation. Direct human intervention belowground may therefore be necessary for the recovery of soil biota once altered.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0040.004
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 head0.079
GPT teacher head0.336
Teacher spread0.256 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2021
Admission routes1
Has abstractyes

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