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Record W2905334229

Water quality concerns and treatment parameters for Armanda Lake

2018· article· en· W2905334229 on OpenAlexaboutno aff
Margarete Kalin

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicIntegrated Water Resources Management
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualityQuality (philosophy)Environmental scienceEnvironmental planningEpistemologyEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Mud Lake receives contaminated groundwater flow originating from the tailings deposit at the abandoned South Bay mine site in Northern Ontario. Mud Lake surface water outflow discharges to the outlet end of Armanda Lake, which then discharges to Confederation Lake at long term monitoring station C11 (see Map1, given at the end of the report). Recent sampling in Armanda Lake has demonstrated unforeseen pH depression in this Lake. This report briefly summarizes historic remedial activity in Mud Lake, describes the recently observed pH depression in Armanda Lake, and recommends a course of action for reversing the pH depression and enhancing the buffering capacity of Armanda Lake sediments through the application of locally available waste wood ash. We feel that treatment is required as soon as possible to prevent further pH decline in Armanda Lake, while we institute longer term controls (i.e. contaminated groundwater treatment) “upstream” in the so-called “Kalin Canyon” and Mud Lake.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.123
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.225
Teacher spread0.205 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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