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Record W2096267911 · doi:10.4141/cjss2011-029

Evaluation of substrate and amendment materials for soil reclamation at a diamond mine in the Northwest Territories, Canada

2012· article· en· W2096267911 on OpenAlexafffundvenueabout
Bonnie Drozdowski, M. Anne Naeth, S. R. Wilkinson

Bibliographic record

VenueCanadian Journal of Soil Science · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of AlbertaAlberta Innovates
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTopsoilAmendmentLand reclamationEnvironmental scienceOrganic matterRevegetationSewage sludgeSoil organic matterNutrientSoil waterSewageEnvironmental engineeringSoil scienceChemistryEcology

Abstract

fetched live from OpenAlex

Drozdowski, B. L., Naeth, M. A. and Wilkinson, S. R. 2012. Evaluation of substrate and amendment materials for soil reclamation at a diamond mine in the Northwest Territories, Canada. Can. J. Soil Sci. 92: 77–88. Mine waste materials with potential for use in soil construction at a diamond mine in the Northwest Territories were evaluated to address physical and chemical limitations for plant establishment, growth and development. Substrates were glacial till, gravel, processed kimberlite, and 50:50 and 25:75 mixes of processed kimberlite and till. Amendments were salvaged topsoil, sewage sludge, inorganic fertilizer and sludge from a water treatment facility. Reclamation soils constructed with these materials were adequate for revegetation. Mixes of processed kimberlite and glacial till enhanced soil structure and diluted adverse concentrations of elements. The original gravel pad, alone or amended, was a suitable substrate for plants. Addition of organic amendments topsoil and sludge, to any substrate, increased organic matter, nutrients and surface water retention. Of amendments evaluated, salvaged topsoil provided the most consistent increase in plant density among substrates. Inorganic fertilizer applied to gravel or till provided results similar to those with topsoil. Sludge had potential to amend mixes of processed kimberlite and till, although results were variable. Sewage was a good source of organic matter, increasing soil water content and macro nutrients. Vegetation response was poor in sewage-amended treatments likely due to combined effects of high copper, molybdenum, phosphorus, selenium, sulphate and zinc.

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)0.005
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 score0.022
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 head0.057
GPT teacher head0.251
Teacher spread0.194 · 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

Citations33
Published2012
Admission routes4
Has abstractyes

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