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Record W2224542897 · doi:10.2495/urs020601

Daily Chloride Contamination Of Lake Ontario By Etobicoke Creek

2002· article· en· W2224542897 on OpenAlexaboutno aff
Rimma Vedom

Bibliographic record

VenueWIT Transactions on Ecology and the Environment · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsBaseflowTributaryEnvironmental scienceHydrology (agriculture)ContaminationWatershedInterflowGroundwaterSurface waterBase flowWater qualityStreamflowEnvironmental engineeringGeographyGeologyDrainage basinCartographyEcology

Abstract

fetched live from OpenAlex

On the heavy urbanized Etobicoke Creek watershed salt crystals and other chlorides are used for roads deicing in winter and for suppressing dust in summer, How are the chlorides from these two sources distributed between surface and ground waters; how subsequently ends up in Lake Ontario, which tributary Etobicoke Creek is? Can surface water monitoring results be interpreted this way? Is it possible to assess daily dynamics of surface and groundwater chlorides based on the available monitoring database? An attempt to quantitatively estimate daily contamination of direct, interand baseflow of a highly urbanized watershed (209 km2) was done using the only source of quality data the monitoring database of Environment Canada (-1 sample/month). Average daily load of chloride by Etobicoke Creek into Lake Ontario was 71,9, highest – almost 1500 tons, Shares of the base, interand direct flow in the monthly loads fluctuated in 18-64, 23-53 and 2-40°A ranges, respectively. The overall year average of chloride loads of the total flow for examined period was 26990 tons breaking into mentioned above components in amounts of 7899, 11252 and 7840 tons, respectively. The result has revealed the predominant contamination of the interflow pathway for Etobicoke Creek.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.005
GPT teacher head0.157
Teacher spread0.152 · 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.

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
Published2002
Admission routes1
Has abstractyes

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