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Record W2482402877 · doi:10.1002/etc.3563

Ecological risk assessment for Pacific salmon exposed to dimethoate in California

2016· article· en· W2482402877 on OpenAlexaff
Melissa L. Whitfield Åslund, Roger L. Breton, Lauren E. Padilla, Michael Winchell, Katie L. Wooding, Dwayne R. J. Moore, R. Scott Teed, Rick Reiss, Paul Whatling

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsCarleton UniversityIntrinsik (Canada)
Fundersnot available
KeywordsDimethoateEnvironmental scienceFisheryEcologyRisk assessmentGeographyOceanographyBiologyPesticide

Abstract

fetched live from OpenAlex

A probabilistic risk assessment of the potential direct and indirect effects of acute dimethoate exposure to salmon populations of concern was conducted for 3 evolutionarily significant units (ESUs) of Pacific salmon in California. These ESUs were the Sacramento River winter-run chinook, the California Central Valley spring-run chinook, and the California Central Valley steelhead. Refined acute exposures were estimated using the Soil and Water Assessment Tool, a river basin-scale model developed to quantify the impact of land-management practices in large, complex watersheds. Both direct effects (i.e., inhibition of brain acetylcholinesterase activity) and indirect effects (i.e., altered availability of aquatic invertebrate prey) were assessed. Risk to salmon and their aquatic invertebrate prey items was determined to be de minimis. Therefore, dimethoate is not expected to have direct or indirect adverse effects on Pacific salmon in these 3 ESUs. Environ Toxicol Chem 2017;36:532-543. © 2016 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 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

Citations14
Published2016
Admission routes1
Has abstractyes

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