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Record W4254307427 · doi:10.2118/03-09-01

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

2003· article· en· W4254307427 on OpenAlexafffundabout
X. Li, J.J. Slaski, Yiwei Feng, M. Fung

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2003
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 waterSoil scienceAgronomyEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Abstract An experiment was initiated in 1997 in northeast Alberta at the Syncrude Canada Ltd. Mildred Lake site to field test an innovative technique for reclamation of oil sand mine tailings. This technique was used to create an aggregated soil material from oil sand tailings. A plant community was successfully established on the soil material created by this technique. However, whether the site would be capable of supporting a self-sustainable ecosystem for the long-term remained a challenging issue. We evaluated the capability of these aggregated oil sand tailings by using biological indicators of the abundance and diversity of soil microbial biomass. Soil respiration rates and soil microbial biomass measurements were used to assess the abundance and activities of soil microbial communities. 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 over time. Increasing the amount of peat moss or muskeg 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 A critical component in the reclamation of oil sand tailings is to create soil materials conducive to the growth of soil microorganisms and, as a result, to stimulate the soil microbial biomass mediated nutrient cycling process following initial reclamation. Soil organic carbon dynamics is at the centre 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 measurements of soil microbial activities as biological indicators for evaluating the capability of reclaimed soil. Materials and Methods Field Site The site, located at Syncrude Canada Ltd. Mildred Lake in northeastern Alberta, was established in 1997. Composite tailings (CT) were used as a sub-material. Five treatments were used for the top 20 cm layer. The composite tailings, amended with various amounts of peat moss, were aggregated using an aggregation technology(1). The control consisted of standard reclamation material (RM) placed over the CT base. The properties of the materials used on this site, and the treatments used, are described in Table 1. Each of the five treatments was replicated four times. A total of 20 (10 m by 10 m) plots arranged in four blocks were set up as a randomized complete block design. After the site was established, the following tree/shrub seedlings were transplanted:

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2003
Admission routes3
Has abstractyes

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