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Record W4205612954 · doi:10.3390/f13010042

Effects of Buried Wood on the Development of Populus tremuloides on Various Oil Sands Reclamation Soils

2022· article· en· W4205612954 on OpenAlexafffund
Kaitlyn E. Trepanier, Laura Manchola-Rojas, Bradley D. Pinno

Bibliographic record

VenueForests · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Natural Resources Limited
KeywordsEnvironmental sciencePeatSoil waterLand reclamationForest floorSoil typeBiomass (ecology)Oil sandsAgronomySoil classificationWoody plantNutrientBotanySoil scienceEcologyBiologyGeography

Abstract

fetched live from OpenAlex

Buried wood is an important but understudied component of reclamation soils. We examined the impacts of buried wood amounts and species on the growth of the common reclamation tree species trembling aspen (Populus tremuloides). In a greenhouse study, aspen seedlings were planted into four soil types, upland derived fine forest floor-mineral mix (fFFMM), coarse forest floor-mineral mix (cFFMM), and lowland derived peat and peat-mineral mix (PMM), that were mixed with either aspen or pine wood shavings at four concentrations (0%, 10%, 20% and 50% of total volume). Height and diameter growth, chlorophyll concentration, and leaf and stem biomass were measured. Soil nutrients and chemical properties were obtained from a parallel study. Buried wood primarily represents an input of carbon to the soil, increasing the C:N ratio, reducing the soil available nitrogen and potentially reducing plant growth. Soil type had the largest impact on aspen growth with fFFMM = peat > PMM > cFFMM. Buried wood type, i.e., aspen or pine, did not have an impact on aspen development, but the amount of buried wood did. In particular, there was an interaction between wood amount and soil type with a large reduction in aspen growth with wood additions of 10% and above on the more productive soils, but no reduction on the less productive soils.

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 score0.538
Threshold uncertainty score0.393

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 studies0.0010.000
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 head0.017
GPT teacher head0.199
Teacher spread0.182 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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