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Record W4295095463 · doi:10.3390/w14172738

Implementing the CCME Water Quality Index for the Evaluation of the Physicochemical Quality of Greek Rivers

2022· article· en· W4295095463 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueWater · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
FundersEuropean Regional Development Fund
KeywordsWater qualityEnvironmental scienceIndex (typography)Quality (philosophy)Scale (ratio)Sampling (signal processing)NutrientHydrology (agriculture)Computer scienceEcologyGeographyGeologyBiologyCartography

Abstract

fetched live from OpenAlex

Water quality indices (WQIs) are efficient tools, globally used for the determination of the quality status of water bodies. In Greece, for almost a decade, the physicochemical quality of water in rivers has been determined by a rigorous, biologically-based, national classification system, developed by the Hellenic Centre for Marine Research (HCMR), through the calculation of a simple water quality index (HWQI) that takes into account six water parameters: five nutrient species and dissolved oxygen. Taking the HWQI as a reference, the present study attempts to implement the Canadian Council of Ministers of Environment Water Quality Index (CCME WQI), which is globally applied and flexible in the number of parameters used, to investigate its possible suitability for Greek rivers, which are characterized by a variety of climatic, geologic, and hydrological conditions and have experienced anthropogenic impact. A large dataset consisting of 111 river sites and multiple sampling campaigns for each site in 2018–2020 were used in the analysis, giving rise to a representative application of the CCME WQI on a national scale. Furthermore, the physicochemical quality results were compared with those derived by the HWQI. Apart from the original equation of the CCME WQI for calculating the classification score, a modified version from the literature was used as well. Moreover, apart from the six conventional parameters, which offered a direct comparison with the output values of the HWQI, the CCME WQI and its modified version were recalculated based on a larger dataset, including four additional physicochemical water parameters. The comparative results from all calculations revealed the conservative behavior of the CCME WQI and confirmed the indications from several other Greek studies. Estimated water quality represented a status that consistently belonged to at least a two-class inferior category than the HWQI, while adequate reductions in this deviation could not be achieved with the modified index or with the increase in the number of parameters used in the analysis. It is thus concluded that the first calculation factor and the class boundaries of the CCME WQI are the limiting factors for successful implementation in Greek rivers, independent of the hydroclimatic, geomorphological, and anthropogenic impact variability across the country.

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.

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)0.008
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 score0.176
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.110
GPT teacher head0.373
Teacher spread0.263 · 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