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Record W2765177889 · doi:10.1002/ieam.2002

A refined ecological risk assessment for California red-legged frog, Delta smelt, and California tiger salamander exposed to malathion

2017· article· en· W2765177889 on OpenAlexaff
Yvonne H. Clemow, Gillian E Manning, Roger L. Breton, Michael Winchell, Lauren E. Padilla, Sara I Rodney, John Hanzas, Tammara L. Estes, Katherine Budreski, Brent N Toth, Katie Hill, Colleen D Priest, R. Scott Teed, Loren D. Knopper, Dwayne R. J. Moore, Christopher T. Stone, Paul Whatling

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsStantec (Canada)Carleton UniversityIntrinsik (Canada)
FundersFMC Corporation
KeywordsTiger salamanderEnvironmental scienceEndangered speciesThreatened speciesRisk assessmentEcologyAquatic ecosystemMalathionProbabilistic risk assessmentMesocosmInvertebratePesticideEcosystemBiologyHabitatLarva

Abstract

fetched live from OpenAlex

The California red-legged frog (CRLF), Delta smelt (DS), and California tiger salamander (CTS) are 3 species listed under the United States Federal Endangered Species Act (ESA), all of which inhabit aquatic ecosystems in California. The US Environmental Protection Agency (USEPA) has conducted deterministic screening-level risk assessments for these species potentially exposed to malathion, an organophosphorus insecticide and acaricide. Results from our screening-level analyses identified potential risk of direct effects to DS as well as indirect effects to all 3 species via reduction in prey. Accordingly, for those species and scenarios in which risk was identified at the screening level, we conducted a refined probabilistic risk assessment for CRLF, DS, and CTS. The refined ecological risk assessment (ERA) was conducted using best available data and approaches, as recommended by the 2013 National Research Council (NRC) report "Assessing Risks to Endangered and Threatened Species from Pesticides." Refined aquatic exposure models including the Pesticide Root Zone Model (PRZM), the Vegetative Filter Strip Modeling System (VFSMOD), the Variable Volume Water Model (VVWM), the Exposure Analysis Modeling System (EXAMS), and the Soil and Water Assessment Tool (SWAT) were used to generate estimated exposure concentrations (EECs) for malathion based on worst-case scenarios in California. Refined effects analyses involved developing concentration-response curves for fish and species sensitivity distributions (SSDs) for fish and aquatic invertebrates. Quantitative risk curves, field and mesocosm studies, surface-water monitoring data, and incident reports were considered in a weight-of-evidence approach. Currently, labeled uses of malathion are not expected to result in direct effects to CRLF, DS or CTS, or indirect effects due to effects on fish and invertebrate prey. Integr Environ Assess Manag 2018;14:224-239. © 2017 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.013
GPT teacher head0.270
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations16
Published2017
Admission routes1
Has abstractyes

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