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Record W2175777452 · doi:10.4141/cjss10019

Salinization of soil over saline-sodic overburden from the oil sands in Alberta

2010· article· en· W2175777452 on OpenAlexvenueaboutno aff
S. Kessler, S. Lee Barbour, K.C.J. Van Rees, Bonnie Dobchuk

Bibliographic record

VenueCanadian Journal of Soil Science · 2010
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsOverburdenLand reclamationSoil salinitySoil waterGeologyVegetation (pathology)Dryland salinityOil sandsSalinitySoil scienceRevegetationSodic soilHydrology (agriculture)Environmental scienceSoil horizonGeotechnical engineeringSoil organic matterAsphaltEcology

Abstract

fetched live from OpenAlex

Saline-sodic mine overburden (also referred to as spoil) removed to access the oil sands in the Athabasca region of Alberta is used as backfill in open pits and is also placed in large upland structures. These deposits are reclaimed with a soil cover to support re-vegetation. The chemistry within reconstructed soil profiles over saline-sodic overburden was investigated to determine the nature and spatial distribution of salts in the soils. Four reclamation treatments were compared: three layered covers (35, 50 and 100 cm thick) and one non-layered cover (100 cm thick). Salts have accumulated in the cover soils 15 to 20 cm above the overburden, raising the electrical conductivity in the lower part of the soil to between 4.5 and 6.0 dS m -1 , which is beyond the acceptable value for vegetation growth. Salt redistribution was not related to slope position and the pattern of salt ingress suggests that diffusion has been the main mechanism driving salt migration into the soils during the initial 4-yr period following placement. Cover thickness did not affect the extent of salt migration, but the overall quality of the thinner covers (35 and 50 cm) for vegetation growth was compromised by the increased salinity levels.

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 score0.535
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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

Citations51
Published2010
Admission routes2
Has abstractyes

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