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Record W6966614527 · doi:10.4224/40003348

Low flow characteristics of Ontario streams

2022· report· en· W6966614527 on OpenAlexaffvenueabout

Bibliographic record

VenueNPARC · 2022
Typereport
Languageen
Field
Topic
Canadian institutionsNational Research Council CanadaInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSTREAMSDeliverableChristian ministrySoftwareObservational studyAuditMandateScale (ratio)

Abstract

fetched live from OpenAlex

In Ontario, analysis of low flows was conducted in 1990 by Cumming Cockburn Limited using observational records from over 340 gauging locations and a software package developed by Inland Waters Directorate (currently Water Survey of Canada) of Environment and Climate Change Canada (ECCC). As the software has become almost obsolete overtime due to tremendous changes in the underlying technology and roughly 35 years of additional data since the development of the report by Cumming Cockburn Limited, the Ministry of Environment, Conservation and Parks (MECP) of Ontario desired to have the software redeveloped in a modern language, along with a user-friendly interface, and all technical reports to be updated. The specific deliverables of the overall project conceived by the MECP were: (1) an updated low flow frequency analysis (LFFA) software, (2) a report pertaining to LFFA of Ontario streams using longer and most recent data, (3) development of a framework for undertaking LFFA considering the effects of future climate change, and (4) a documented review of regional LFFA techniques, with a focus on ungauged locations. The National Research Council Canada (NRC) headed this effort through an inter-departmental agreement between the MECP and the NRC. This report pertains to item 2 above and therefore contains updated information on low flow analysis for Ontario streams based on longer observational records, where available, from the entire hydrometric network managed by ECCC across Ontario. Theoretical information on data screening procedures and distribution fitting methods was derived mainly from the report prepared by Cumming Cockburn Limited and key technical documents that were referred to at the time. Some additional insights from the published literature that have become available since then are also considered. In the reports by Cumming Cockburn Limited, the analyses were organized based on five regional partitions of Ontario. The spatial demarcation of these regions is no longer available with the MECP and therefore five administrative regions of Ontario are used in this report to organize results of low flow analyses and to satisfy project objectives.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.145
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.265
Teacher spread0.236 · 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
Published2022
Admission routes3
Has abstractyes

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