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Record W2749267175 · doi:10.1139/cjss-2017-0037

Carbon, nitrogen and phosphorus release from peat and forest floor-based cover soils used during oil sands reclamation

2017· article· en· W2749267175 on OpenAlexaffvenueabout
Sylvie A. Quideau, Charlotte E. Norris, Frédéric Rees, Miles Dyck, Najmeh Samadi, Se‐Woung Oh

Bibliographic record

VenueCanadian Journal of Soil Science · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPeatLand reclamationEnvironmental scienceOil sandsPhosphorusSoil waterNitrogenForest floorCarbon fibersCover (algebra)Soil scienceChemistryGeographyArchaeology

Abstract

fetched live from OpenAlex

Reclamation practices in the oil sands region of Alberta involve the reconstruction of soil profiles using a combination of salvaged mineral substrates and organic-matter-rich surface materials, including peat–mineral mix (PM) and forest floor – mineral mix (FFM). The successful re-establishment of vegetation on reclaimed sites is for a large part dependent on the nutrients these materials can provide. Hence, the overall objective of this study was to compare carbon (C), nitrogen (N), and phosphorus (P) release rates from PM and FFM materials used to cap reconstructed sandy soils. A 325 d laboratory incubation was conducted to measure these rates. The two materials released comparable amounts of N on a per kilogram of soil basis (111–118 mg N kg−1). However, when results were normalized based on each material’s organic C content, N release was six times greater for FFM than for PM, in accordance with results of previous studies. In addition, overall C mineralization and P release rates were over one order of magnitude higher with FFM than with PM. As opposed to N, however, P release seemed to be controlled more by abiotic processes than by organic matter mineralization. While the FFM material overall released more N and P, it also degraded faster; in comparison, PM may provide a smaller but more stable release of N.

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 score0.081
Threshold uncertainty score0.820

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 studies0.0010.001
Scholarly communication0.0010.001
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 head0.013
GPT teacher head0.189
Teacher spread0.176 · 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

Citations15
Published2017
Admission routes3
Has abstractyes

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