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Record W3188626444 · doi:10.1080/11956860.2021.1943931

Natural recovery of vegetation on reclamation stockpiles after 26 to 34 years

2021· article· en· W3188626444 on OpenAlexafffundvenue
Brenda Erin Shaughnessy, Amalesh Dhar, M. Anne Naeth

Bibliographic record

VenueEcoscience · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Alberta
FundersSuncor Energy Incorporated
KeywordsRevegetationLand reclamationPeatVegetation (pathology)ShrubPlant communityEcologyEcological successionTaigaEnvironmental scienceBorealBiology

Abstract

fetched live from OpenAlex

Stockpiling of soils is essential for reclamation after oil sands mining, and can influence revegetation through degradation of seed viability and soil quality. Three peat-mineral stockpiled areas in upland boreal forest, aged 26 to 34 years, were assessed for effects on soil, vegetation, and successional status to study the natural recovery of vegetation. Six upland (five native, one non-native) and one lowland native species community types were identified where non-vascular had more communities than vascular plants. Upland boreal species that were likely not present in the soil seed bank, colonized the sites relatively quickly with a plant community of early to mid successionals, including persistence of a lowland species (Amblystegium serpens) and non-natives. Presence of a non-native community (Melilotus officinalis) 26 to 34 years after reclamation can be concerning. Stockpiled soil texture (clay) and tall shrub stem density were most influential on plant community development. Stem density (DBH > 5 cm), self-thinning of early successional species (Salix, Betula papyrifera), and presence of climax species (Picea glauca) resembled the successional trend of natural boreal forests in the region. Results from this study suggest natural recovery of upland boreal forest on lowland peat substrate is possible and can support evolving plant communities.

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 score0.544
Threshold uncertainty score0.530

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 head0.007
GPT teacher head0.219
Teacher spread0.213 · 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

Citations10
Published2021
Admission routes3
Has abstractyes

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