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Record W2296495903 · doi:10.14288/1.0058589

Environmental effects of manganese and proposed guidelines to protect freshwater life in British Columbia

2009· article· en· W2296495903 on OpenAlexaffabout
Peter Samuel Reimer

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsManganeseEnvironmental planningEnvironmental scienceEnvironmental protectionEnvironmental resource managementChemistry

Abstract

fetched live from OpenAlex

Manganese is a naturally occurring substances that is present in surface waters and biota. Aquatic organisms have exhibited toxic responses to manganese in surface waters and regulatory bodies in some jurisdictions have established guidelines for levels of manganese in surface water to protect aquatic life. In British Columbia, a guideline of 0.1 mg/L was established by the Ministry of Environment, Lands and Parks, although it was recognized that the scientific data on which this guideline was based were weak. Toxicity tests applicable to aquatic life in B.C. waters were commissioned to strengthen the relevant data base and to apply the British Columbia procedures for deriving water quality criteria in an effort to establish more defensible guidelines for the protection of aquatic life in B.C. Acute and chronic toxicity tests were conducted on fish, invertebrates and freshwater algae. Acute tests included 48 and 96 hour LC50's, while chronic tests included reproduction, growth and survival endpoints. A range of organisms was chosen in order to evaluate the range of sensitivities to manganese.. The possible relationship between water hardness and toxicity to manganese was also investigated at water hardnesses of 25, 100 and 250 mg/L CaC03. Data were also gathered from literature sources in support of the new toxicity information. Both acute and chronic studies were identified for fish species resident in B.C. fresh waters. The collective data were evaluated for suitability with respect to the B.C. water quality guideline derivation process. Toxicity t test data that met the requirements for use in guideline derivation were screened for sensitivity in order to fulfill the objective of developing a guideline protective of the most sensitive aquatic organisms. A pattern emerged whereby the concentrations of manganese at which adverse effects were observed increased with increasing water hardness. This pattern was identified in both the literature data and all but one of the new toxicity tests commissioned by the Ministry of Environment, Lands and Parks. Acute and chronic regression equations were developed using the most sensitive data for various (in both cases six) water hardness values. The acute equation was Y = 0.044IX +1.81 and the chronic equation was Y = 0.0176 + 2.42, where X = water hardness in mg/L CaC03 and Y = Mn concentration in mg/L. The equations were used to predict manganese concentrations at water hardness increments of 25 mg/L CaC03 over the hardness range of 25-325 mg/L CaC03, a range that encompasses the vast majority of B.C. surface waters. A factor of safety of 0.25 was applied to the predicted concentrations to account for uncertainty and was based on scientific judgement and the strength of the data set used in the derivation process. The resulting acute manganese concentrations ranged from 0.6 to 3.8 mg/L and are proposed as guidelines for exposure of <96 hours. The resulting chronic manganese concentrations ranged from 0.6 to 1.9 mg/L and are proposed as guidelines for exposure exceeding 96 hours. While B.C. and other surface water data indicate that manganese rarely exceeds concentrations of 1 mg/L, it is recognized that natural events may result in periodic increases. The application of guidelines intended to protect aquatic life from anthropogenic sources of manganese should reflect this in the sampling methodology requirements.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.731

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)0.0010.000

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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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

Citations3
Published2009
Admission routes2
Has abstractyes

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