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Record W2275058037 · doi:10.14288/1.0042437

Reclamation research and monitoring at Highland Valley Copper

2009· article· en· W2275058037 on OpenAlexaff
C. E. Jones, Mark Freberg, Bob A. Hamaguchi, Justin Straker

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLand reclamationEnvironmental scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

To meet the goals of revegetating land to a self-sustaining state, using appropriate plant species and achieving levels of land productivity not less than existed prior to mining, Highland Valley Copper has undertaken over 20 years of reclamation research and monitoring. The evaluation of the results of these studies has provided important feedback that is used to modify and enhance the reclamation product. Monitoring results have also been used to modify and enhance the reclamation product. Monitoring results have also been used to determine if progress is proceeding toward the desired goals and provide early warning signs of potential problems. Benchmark values have been developed that indicate the need for remedial action and others that indicate the expected trajectory of the revegetation to an acceptable product. Parameters that have been measured for forage areas include species composition, nutrient content and biomass production. On areas planted with trees and shrubs, parameters measured include survival, growth and stocking densities. All of these parameters have been measured systematically across the revegetated areas and over time. Initial assessments of revegetated areas are conducted two years following establishment and a second assessment is conducted three years after the withdrawal of maintenance fertilizer application. Based on the results of this monitoring and other directed research studies, Highland Valley Copper can be shown to meet their revegetation goals and satisfy the land use objectives for the property.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.571

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)0.0000.000

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 head0.026
GPT teacher head0.222
Teacher spread0.196 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

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