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Record W4384828376 · doi:10.23818/limn.43.15

A comparative analysis of water quality guidelines for fluoride in Canada and Spain

2023· article· en· W4384828376 on OpenAlexaboutno aff
Julio A. Camargo

Bibliographic record

VenueLimnetica · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFluoride Effects and Removal
Canadian institutionsnot available
Fundersnot available
KeywordsFluorideWater qualityEuropean unionEnvironmental sciencePollutionWater pollutionInvertebrateEnvironmental protectionEnvironmental chemistryEcologyChemistryBiologyBusiness

Abstract

fetched live from OpenAlex

Although anthropogenic fluoride (F−) pollution is a serious worldwide environmental problem, only a few countries have currently established national water quality criteria for the protection of freshwater biota. Since Canada is a global leader in biodiversity conservation that exhibits restrictive water quality benchmarks, I carry out a comparative analysis of water quality guidelines for fluoride in Canada and Spain. The Canadian water quality benchmark of 0.12 mg F−/l (maximum allowable concentration) prevents Canada’s fresh waters from significant adverse events of fluoride pollution, thereby protecting sensitive native aquatic invertebrates and adult-migrating Pacific salmon. By contrast, the Spanish water quality benchmark of 1.7 mg F−/l (annual mean concentration) allows not only continuous levels of fluoride pollution more than six times higher than natural fluoride concentrations in the fresh waters of mainland Spain, but also much higher discontinuous levels of fluoride pollution (> 15 mg F−/l). This unacceptable scenario is contrary to the current environmental goal of “zero pollution” in the European Union. In view of the existing toxicological data, a Spanish water quality guideline of 0.15−0.3 mg F−/l (maximum allowable concentration) seems much more reasonable. The recommended water quality guideline for fluoride would much better protect sensitive native fish and invertebrate species, and prevent significant bioaccumulation of fluoride in tolerant freshwater organisms.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.206

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.325
Teacher spread0.277 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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