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Record W1975591919 · doi:10.2118/2001-072

Evaluation of the Capability of Aggregated Oil Sands Mine Tailings: Biological Indicators

2001· article· en· W1975591919 on OpenAlexafffundabout
X. Li, Yu Feng, Jan J. Ślaski, M. Fung

Bibliographic record

VenueCanadian International Petroleum Conference · 2001
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsSyncrude (Canada)University of AlbertaSocial Sciences and Humanities Research Council
FundersSyncrude
KeywordsTailingsEnvironmental scienceLand reclamationBiomass (ecology)Oil sandsSoil carbonSoil waterEcosystemPeatMicrobial population biologySoil scienceEcologyGeologyChemistryBiology

Abstract

fetched live from OpenAlex

Abstract An experiment was initiated in 1997 in northeast Alberta at Syncrude Canada Ltd. Mildred Lake site to field test an innovative technique for reclamation of oil sands mine tailings. This technique was used to create an aggregated surface soil material from oil sand tailings. Plant community was successfully established on soil material created by this technique. However, whether the site would be capable of supporting a self-sustainable ecosystem for the long-term remained unknown. We evaluated the capability of these aggregated oil sands tailings by using biological indicators because the abundance and diversity of soil microbial biomass is a good measure of the health of soil-plant ecosystem. Soil respiration rates and soil microbial biomass were used to assess the abundance and activities of soil microbial communities. In addition, the ability of soil microbial biomass to utilize a diverse range of carbon substrates was used to assess the diversity of soil microbial communities. Soil biological activity increased with increasing growth of plant biomass and with time. Increasing amount of peat moss incorporated into the soil during reclamation resulted in higher organic carbon and nitrogen content and caused an increase in abundance and diversity of soil microbial biomass. These results indicate that measurements of soil respiration and substrate utilization by soil microbial communities may be used as biological indicators for assessing the capability of reclaimed soils. Introduction One active area of land reclamation research is to compare and synthesize patterns and processes in reclaimed soils and to assess their capability for supporting a self-sustaining ecosystem. Such comparisons and synthesis work best only when the measurements made are comparable and repeatable (Robertson et al., 1999). Using a standard methodology is a key to addressing many questions regarding longterm sustainability of reclaimed land. A critical component in the reclamation of oil sands tailings is to create soil materials conducive to the growth of soil microorganisms and as a result, to stimulate soil microbial biomass mediated nutrient cycling process following initial reclamation. Soil organic carbon dynamics is at the center of these processes. Soil microbial biomass is largely responsible for the decomposition of soil organic matter and litters that contribute to soil nutrient pools through mineralization. Additionally, certain soil microorganisms form symbiotic associations (mycorrhizae, nodules) with plants and contribute to the overall success of plant growth. Thus the success of planting for reclamation purposes is affected by, and may be contingent upon, the quality and quantity of soil microbial communities and their activities. This paper presents the use of a few simple measurements of soil microbial activities as biological indicators for evaluating the capability of reclaimed soil. MATERIALS AND METHODS Field Site The site, located at the Syncrude Canada Ltd. Mildred Lake in northeastern Alberta, was established in 1997. Composite tailings (CT) weres used as a sub-material. Five treatments were used for the top 20 cm layer. The composite tailings, amended with various amount of peat moss, were aggregated using an aggregation technology (Li and Fung, 1998).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

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)0.0010.000

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 head0.029
GPT teacher head0.248
Teacher spread0.219 · 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.

Study designSimulation or modeling
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

Citations5
Published2001
Admission routes3
Has abstractyes

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