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Record W2041788648 · doi:10.1038/npre.2008.1740.1

Potential Impacts of Tailings and Tailings Cover Fertilization on Arsenic Mobility in Surface and Ground Waters

2008· preprint· en· W2041788648 on OpenAlex
Sierra Rayne, Kaya Forest

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNature Precedings · 2008
Typepreprint
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsOkanagan CollegeThompson Rivers University
Fundersnot available
KeywordsTailingsRevegetationArsenicEnvironmental scienceLand reclamationMining engineeringGeologyEcologyChemistry

Abstract

fetched live from OpenAlex

Abstract A number of mining sites worldwide, particularly gold mines, have tailings management facilities (TMFs) that contain high levels of arsenic. Current closed mine site regulatory agencies tend to prefer revegetation of TMFs as part of the mandated reclamation activities. At many sites, often in polar regions, vegetation is difficult to establish either directly on the tailings or on the coarse-rock covers due to nutrient poor soils, phytotoxicity problems, and/or a less than optimum climate. Addition of phosphorus-based fertilizers to the tailings and/or cover material is commonly considered in order to promote the revegetation process and – ideally – allow the site owners to discharge their closure duties as rapidly as possible. However, due to the similar geochemistry of arsenic and phosphorus oxyanion species, this type of mine closure strategy may have unintended consequences regarding arsenic mobility on and off the site. This document reviews the current state-of-the-art regarding mobilization of arsenic by phosphate ions, and identifies relevant risks and opportunities of using this information to better manage closed mine sites.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
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 integrity0.0010.001
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 head0.237
Teacher spread0.230 · 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