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Record W7008830231

Deriving receiving-water based, point-source effluent requirements for Ontario waters

2019· report· en· W7008830231 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2019
Typereport
Languageen
FieldSocial Sciences
TopicWater Resources and Governance
Canadian institutionsnot available
FundersMinistry of Education, IndiaHellenic Ministry of Environment and Energy
KeywordsEffluentWater qualityChristian ministryPollutionSewageWater pollutionSurface water
DOInot available

Abstract

fetched live from OpenAlex

This report describes the procedures used by the Ontario Ministry of Environment and Energy (MOEE) to establish receiving-water based effluent requirements for point source discharges to surface waterbodies. The procedures are based on the policies contained in Water Management - Policies, Guidelines and Provincial Water Quality Objectives (MOEE. 1994). The Provincial Water Quality Objectives (PWQO) play a major role in the development of effluent limits. Specific PWQO are listed in Water Management. While primary emphasis of this document relates to setting treated effluent discharge limits from point sources of pollution such as industries and sewage treatment plants, it is recognized that other non-point or diffuse sources of pollution - urban, rural and atmospheric, can contribute substantially to water quality degradation and use impairment. Procedures for managing non-point sources of pollution are not addressed in this report. The implementation procedures described in this report support Water Management and provide general direction on a wide range of procedures for determining effluent requirements for Certificates of Approval or other legal documents. The text is not all inclusive, and it is strongly suggested that proponents or their consultants contact the appropriate MOEE Regional Surface Water Assessment staff to determine if additional site specific-conditions would apply to the effluent discharge in question.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.259
Teacher spread0.217 · 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 designNot applicable
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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