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Record W4308639689 · doi:10.1002/etc.5519

Physical–Chemical Recovery of a Montane Stream After Remediation of Acid Mine Drainage: Timing and Extent After Turning off the Tap

2022· article· en· W4308639689 on OpenAlexfundno aff
Joseph S. Meyer, Evan H. Lloyd, Shaun Bevers, James F. Ranville

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsnot available
FundersRio TintoInternational Zinc AssociationInternational Copper AssociationNational Institute of Environmental Health SciencesCopper Development Association
KeywordsSnowmeltEnvironmental chemistryEnvironmental remediationAcid mine drainageLimeEnvironmental scienceSurface waterChemistryParticulatesContaminationHydrology (agriculture)Surface runoffEnvironmental engineeringEcologyGeology

Abstract

fetched live from OpenAlex

We monitored physical-chemical conditions in the North Fork of Clear Creek in Colorado (USA) before, during, and after the start of remediation (lime treatment) to remove metals from two major inputs of acid mine drainage (AMD) water. In addition, we analyzed historical monitoring data that extended back more than two decades. Concentration-discharge (C-D) and load-discharge (L-D) plots accounted for discharge dependence in concentrations and loads of metals, major ions, and other water chemistry parameters. Total and dissolved concentrations, and loads of the metals decreased after remediation began, with the largest decreases usually during low stream flow. However, postremediation concentrations and loads remained slightly to considerably higher than reference, probably because of unidentified groundwater seeps and/or small surface flows. Dissolved Cu concentrations decreased much less than total Cu concentrations, because the percentage of total Cu in the dissolved phase increased considerably as particulate Fe (PFe) concentration decreased. We conclude that 1) water chemistry can change to a new steady state or pseudo-steady state relatively quickly after major AMD inputs to a stream are remediated; 2) elevated flows during snowmelt and rainfall periods can mobilize additional amounts of major ions and metals, resulting in in-stream concentrations that are manifestations of both dilution and mobilization; 3) although lime treatment of AMD-related waters can decrease metal concentrations, it does not decrease elevated concentrations of major ions that might impair sensitive stream invertebrates; 4) although Fe is toxic to aquatic organisms, PFe adsorbs other metals and thereby provides protection against their toxicity; and 5) use of C-D and L-D plots and element ratios can indicate the presence of unidentified AMD inputs to a stream. Environ Toxicol Chem 2023;42:495-511. © 2022 SETAC.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.061

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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