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Record W6922367408 · doi:10.1139/cjss2010-028

Phenolic profiles in natural and reconstructed soils from the oil sands region of Alberta

2012· article· en· W6922367408 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2012
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsPeatLand reclamationSoil waterOrganic matterPhenolsDecompositionSoil organic matterExtraction (chemistry)

Abstract

fetched live from OpenAlex

Turcotte, I. and Quideau, S. A. 2012. Phenolic profiles in natural and reconstructed soils from the oil sands region of Alberta. Can. J. Soil Sci. 92: 153-164. This research was conducted in the Athabasca oil sands reclamation area of northeastern Alberta, where land reclamation entails reconstruction of soil-like profiles using salvaged materials such as peat and mining by-products. Successful reclamation is in part dependent on the quality of the organic capping of these reconstructed soils. This study investigated organic matter composition between reconstructed and natural soils. Soil samples (0-10 cm) were taken from 45 plots to represent a range of reclaimed and undisturbed sites. The botanical origin of soil organic matter was characterised through cupric oxide oxidation, which yields lignin monomers hypothesized to reflect vegetation inputs and extent of decomposition based on time since reclamation. Additional soil organic matter parameters were obtained using acid hydrolysis, physical separation and ramped cross polarisation C-13 nuclear magnetic resonance techniques. Yields of vanillyl phenols, coumaryl phenols, p-hydroxy phenols, summed lignin phenols and total phenolic constituents were significantly higher in natural soils than in reconstructed soils. We suggest that there may be an accelerated decomposition of peat phenols in reconstructed soils, which would leave only those phenols representative of the vascular plant history accumulated in the initial peat profile.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.711

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 head0.142
GPT teacher head0.232
Teacher spread0.090 · 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 designBench or experimental
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
Published2012
Admission routes1
Has abstractyes

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