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Record W4392966283 · doi:10.3389/fenvs.2024.1303705

Toxicology, environmental chemistry, ecotoxicology, and One Health: definitions and paths for future research

2024· article· en· W4392966283 on OpenAlexaff
Sébastien Sauvé

Bibliographic record

VenueFrontiers in Environmental Science · 2024
Typearticle
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsEcotoxicologyEnvironmental toxicologyEnvironmental chemistryEnvironmental scienceEnvironmental healthChemistryMedicineToxicityOrganic chemistry

Abstract

fetched live from OpenAlex

The definitions of toxicology, environmental toxicology, environmental chemistry, environmental risk, and ecotoxicology are closely related and sometimes used as synonyms, whereas One Health is a more recent, complementary concept. This contribution examines the origins of the usages of these terms, explores their interchangeability (whether appropriate or not), and proposes some paths to better define each. The usage of these terms is evolving, and current research and paradigms are progressing toward the integration of broader, more integrative perspectives, such as the One Health approach. One Health is a holistic approach that helps link and integrate work on environmental and human health impacts. Definitions and research should not necessarily strive to segregate human vs. environmentally focused work, and most of the problems are complex and interconnected. Future research endeavors and funding programs must better reflect the multidisciplinary nature of environmental toxicology, and more broadly, One Health research and environmental research must recognize the interrelationships of human health, environmental health, ecotoxicology, and a multitude of geochemical, microbiological, and ecological processes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.967

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 studies0.0000.003
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 head0.164
GPT teacher head0.395
Teacher spread0.231 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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