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Record W7134731944

Synthetic Water Calibration for Water Quality Parameters & Water Treatment Program Validation

2019· article· en· W7134731944 on OpenAlexaff
Lynda Smithard

Bibliographic record

VenuecIRcle (University of British Columbia) · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEffluentWater treatmentWater qualitySump (aquarium)Pilot plantSurface waterScale (ratio)LeachateWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

To support the Environmental Assessment (EA) of a new gold mine project in BC, McCue completed the preliminary design of a mine water treatment plant (WTP) using a water profile modeled by others for the future open pit sump water. The water profile is complex and the treatment plan includes heavy metals removal by chemical precipitation. Plant effluent quality was initially predicted largely based on published theoretical heavy metal solubility data. To address uncertainty with the treatment process and improve the inputs for the impact assessment model (by others), McCue created a synthetic water sample from field leachate samples and laboratorygrade salts to match the modeled plant inlet water profile. The synthetic water sample was used to validate the water treatment process at a bench scale and provide effluent quality data for impact assessment modeling. The bench scale test program also provided valuable data needed in the future to advance the design of the mine water treatment plant from preliminary to detailed. Data from the bench scale work reduced uncertainty as to what could be achieved with water treatment at the project site and what impact the treated water would have on the local environment. This was important in satisfying both the regulators reviewing the EA and the project stakeholders, including local First Nations. The bench scale test results and data from an ensuing full-scale treatment plant at another project site have also contributed to the body of knowledge for heavy metals precipitation treatment performance. For instance, previously, literature indicated that no or negligible removal could be achieved for copper. McCue’s work has provided valuable data for copper that could benefit EA work at other mine sites globally. McCue will present the synthetic sample method, bench scale test program results, and how they related to the detailed design and performance of a full-scale metals precipitation plant that offered an economical treatment program for complex water containing high levels of salts in addition to heavy metals.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.011
GPT teacher head0.205
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2019
Admission routes1
Has abstractyes

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