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Record W1962742013 · doi:10.1002/ieam.1694

A statistical evaluation of the safety factor and species sensitivity distribution approaches to deriving environmental quality guidelines

2015· article· en· W1962742013 on OpenAlexaffabout
Barry Zajdlik

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsGumbel distributionEnvironmental scienceQuality (philosophy)StatisticsDistribution (mathematics)Sample (material)Christian ministryEconometricsMathematicsChemistryPolitical scienceExtreme value theory

Abstract

fetched live from OpenAlex

Abstract The species sensitivity distribution (SSD) distribution approach to estimating water quality guidelines (WQGs) is the preferred method in all jurisdictions reviewed (Australia, Canada, New Zealand, Organisation for Economic Co-operation and Development [OECD] members, South Africa, United States) and is one of the recommended methods for European Commission members for 33 priority and priority hazardous substances. In the event that jurisdiction-specific criteria for data quality, quantity, and taxonomic representation are not met, all of these jurisdictions endorse the use of additional safety factors (SFs) applied to either the SSD-based WQG or, the lowest suitable toxicity test endpoint. In Canada, the British Columbia Ministry of Environment endorses this latter approach as the preferred approach in the belief that so-derived WQGs are more protective than SSD-based WQGs. The level of protection afforded by the latter SF approach was evaluated by statistically sampling minima from random samples of the following distributions: normal, Gumbel, logistic, and Weibull, using a range of coefficients of variation (cVs) and applying the SFs of 2 or 10 used in British Columbia. The simulations indicate that the potentially affected fraction of species (PAF) can be as high as 20%, or, approach 0%. The PAF varies with sample size and CV. Because CVs can vary systematically with mode of toxic action, the PAF using SF-based WQGs can also vary systematically with analyte class. The varying levels of protection afforded by SF-based WQGs are generally inconsistent with the common water quality management goal that allows for a small degree of change under long-term exposure. The findings suggest that further efforts be made to develop high-quality WQGs that support informed decision making and are consistent with the environmental management goal instead of using SFs in the hope of achieving an acceptable but unknown, degree of environmental protection. Integr Environ Assess Manag 2016;12:380–387. © 2015 SETAC Key Points Application of safety factors to the “lowest available” toxicity test endpoint is either preferred or fall-back method for estimating environmental quality guidelines. It is believed that the safety factor method is more protective than other methods such as using the 5th percentile of a species sensitivity distribution. The level of protection using the safety factor method is inconsistent, varying with dataset sample size, degree of variability, and the underlying statistical distribution of the toxicity test endpoint. The safety factor approach can protect (as a limit) 100% of species or less than 55% of species.

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)0.001
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 score0.463
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.208
GPT teacher head0.314
Teacher spread0.106 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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