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Record W2914067274 · doi:10.4095/313598

Groundwater research in southern Ontario by Environment and Climate Change Canada

2019· report· en· W2914067274 on OpenAlexaboutno aff
Jim Roy, Jacob Spoelstra, Dale R. Van Stempvoort, Victoria R. Propp

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwaterClimate changeEnvironmental scienceWater resource managementHydrology (agriculture)GeographyPhysical geographyEnvironmental resource managementGeologyOceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Over the past decade, the groundwater-specific research of Environment and Climate Change Canada has focused on groundwater transport of pollutants to surface waters. Within southern Ontario, the key pollutant of interest has been phosphorus, due to its link to eutrophication and harmful algal blooms of the Great Lakes and its watershed. Our work has targeted point sources, such as domestic wastewater septic systems, as well as broader-scale inputs within both urban and rural landscapes. Other contaminants have also received interest, including chloride (road salt), metals, and both legacy and emerging organic contaminants (e.g., pharmaceuticals, per- and polyfluoralkyl substances (PFAS), organophosphate flame retardants, etc.), especially in urban environments. A new study is looking at a range of these chemicals in old, closed landfills, of which thousands reside across southern Ontario. These substances pose a toxicity risk to aquatic ecosystems in the receiving environment and may spread more broadly from there. This work has largely employed two methodological "tools", i) surface water receptor-targeted groundwater sampling, and ii) the analysis of artificial sweeteners. The former allows for rapid acquisition of a large number of samples at relatively moderate expense in comparison to the use of wells. It also provides more exact information on the concentrations or mass of contaminants impacting the receptor. Artificial sweeteners can serve as a tracer of wastewater and landfill leachate, thus being a proxy for the possible presence of other wastewater or leachate contaminants and allow some quantification of their potential inputs. Their presence can also guide targeted sampling of more costly analytes. Here we provide a brief overview of our recent research in southern Ontario with some key examples of how these two tools have provided important results on the risks posed by groundwater-sourced pollutants to Great Lakes ecosystems.

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 score0.922
Threshold uncertainty score0.568

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.015
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.149
GPT teacher head0.280
Teacher spread0.131 · 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
Published2019
Admission routes1
Has abstractyes

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