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Record W2116950955 · doi:10.2166/wqrjc.2012.042

Regulations, legislation, and guidelines for artificial surface water and groundwater tracer tests in Canada

2012· article· en· W2116950955 on OpenAlexaffabout
Christian Wolkersdorfer, Jenna LeBlanc

Bibliographic record

VenueWater Quality Research Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsCape Breton University
Fundersnot available
KeywordsLegislationTRACERGroundwaterWater qualityEnvironmental planningTest (biology)Environmental scienceEnvironmental protectionSurface waterEnvironmental resource managementEngineeringEnvironmental engineeringLawPolitical scienceEcology

Abstract

fetched live from OpenAlex

This paper describes Canadian federal and provincial regulations, legislation, and guidelines for artificial tracer tests, where substances are released into water, and provides a world-wide comparison. Alberta is currently the only Canadian province with guidelines and regulations relating to those tests. None of the other provinces have specific tracer test regulations in place, though the injection of artificial substances into waters is covered by Section 36(3) of the federal Fisheries Act. Newfoundland and Labrador, the Northwest Territories, and Nunavut sometimes require a permit to conduct a tracer test, and Quebec is planning to implement guidelines and regulations based on Michigan/USA Environmental Quality guidelines. In each case Fisheries and Oceans Canada (DFO), Environment Canada, and the Provincial environment departments should be contacted and the proposed test described as detailed as necessary. We present potential tracers, such as uranine (sodium fluorescein), or Rhodamine WT, that can be used in artificial tracer tests. This study is the result of contacting personnel from organizations such as Environment Canada, Fisheries and Oceans Canada, provincial departments of environment, researchers, and consultants.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.195
GPT teacher head0.402
Teacher spread0.207 · 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

Citations10
Published2012
Admission routes2
Has abstractyes

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