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Record W3011137070 · doi:10.2166/wqrj.2020.007

Development of a water quality index for watercourses downstream of harvested peatlands

2020· article· en· W3011137070 on OpenAlexafffundabout
Hermine Betis, André St‐Hilaire, Claude Fortin, Sophie Duchesne

Bibliographic record

VenueWater Quality Research Journal · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologies
KeywordsPeatWater qualityEnvironmental scienceSTREAMSHydrology (agriculture)DrainageGeologyEcologyComputer science

Abstract

fetched live from OpenAlex

Abstract This study aimed to adapt the Water Quality Index of the Canadian Council of Ministers of the Environment (CCME WQI) for its application to water quality assessment of drainage water and watercourses downstream of peat harvesting operations. It integrates different parameters that potentially reflect the overall water quality condition of a stream. Thus, it is calculated using multivariate water quality data and accounts for their conformity with respect to water quality guidelines. Adaptation of the index proceeded to identify, through a literature review, the physico-chemical parameters that may change due to peat harvesting. The CCME WQI was used to compare water quality of receiving watercourses to that of streams located within a 200 km radius from the study sites in three regions of Quebec. The availability of water quality data guided the selection of parameters among those identified. They are ammonia, conductivity, pH and suspended sediment concentrations. Results indicated a significant difference between WQI values of water from harvested peatlands and those of streams in two of the three regions studied. Results have also shown that it is the pH guideline that is not respected in most cases for harvested peatlands. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).

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)0.007
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.165
GPT teacher head0.400
Teacher spread0.235 · 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 designBench or experimental
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

Citations8
Published2020
Admission routes3
Has abstractyes

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