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Record W2901067914 · doi:10.3390/f9120731

Growth Response of Aspen and Alder to Fresh and Stockpiled Reclamation Soils

2018· article· en· W2901067914 on OpenAlexafffundabout
Kwadwo Omari, Sanatan Das Gupta, Bradley D. Pinno

Bibliographic record

VenueForests · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of AlbertaNatural Resources CanadaCanadian Forest Service
FundersCanadian Natural Resources Limited
KeywordsAlderAgronomyEnvironmental scienceRevegetationSoil waterLand reclamationBiomass (ecology)PeatBotanyBiologyEcology

Abstract

fetched live from OpenAlex

Soil stockpiling is a common reclamation practice used in oil sands mining in the boreal forest region of Canada to conserve soil resources; but stockpiling may have detrimental effects on soil quality and plant growth. We examined growth response of trembling aspen (Populus tremuloides Michx.), a fast-growing early successional tree, and green alder (Alnus viridis (Chaix) DC. ssp crispa (Ait.) Turrill), a nitrogen-fixing shrub, to stockpiling and fertilization treatments on two reclamation soils (forest floor mineral mix (FFMM) and peat mineral mix (PMM)). Aspen and alder seeds were planted and their growth monitored for four months in the greenhouse. We found that unfertilized stockpiled FFMM supported significantly higher aspen and alder aboveground biomass than the other fresh and stockpiled soils. Phosphorus and potassium supply rates were highest in stockpiled FFMM and were positively correlated with aboveground plant biomass. There was no significant difference in aspen and alder aboveground biomasses between unfertilized fresh FFMM and PMM soils. Aspen grown in combination with nitrogen-fixing alder did not experience competition or facilitation except on fresh PMM, where aspen height declined. Fertilization increased both aspen and alder growth and eliminated differences in growth between soil types and stockpiling treatments. Our study showed that individual soil properties are more important for revegetation purposes than type of soil or stockpiling treatment.

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 score0.011
Threshold uncertainty score0.433

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 head0.008
GPT teacher head0.237
Teacher spread0.228 · 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

Citations3
Published2018
Admission routes3
Has abstractyes

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