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Record W2063300845 · doi:10.1080/15287390701429505

Toxicology in Australia: A Key Component of Environmental Health

2007· review· en· W2063300845 on OpenAlexaff
Brian G. Priestly, Peter Di Marco, Malcolm Sim, Michael R. Moore, Andrew Langley

Bibliographic record

VenueJournal of Toxicology and Environmental Health · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsSummitGovernment (linguistics)Public healthPromotion (chess)Environmental planningSustainable developmentPolitical scienceEarth SummitEnvironmental healthRisk assessmentBusinessEnvironmental resource managementMedicineGeographyManagementEnvironmental science

Abstract

fetched live from OpenAlex

Managing public concerns relating to chemical exposures can consume substantial public health resources, particularly as the scientific basis around these issues is often contentious. Toxicology remains underrecognized as a public health discipline in Australia, although Australian toxicologists are making significant contributions from academia, government, and the commercial sector toward assessing the level of risk and protecting the community from environmental hazards. Internationally, the growth of environmental toxicology and the promotion of sound science in risk assessment as a basis for making regulatory decisions have been, to some extent, driven by the outcomes of the 1992 UNCED Conference on Sustainable Development (Rio Summit) and its Chapter 19 Agenda 21 activities. The promotion of safe chemical management practices and the need for global strengthening of capabilities in toxicology are among the initiatives of the Intergovernmental Forum on Chemical Safety (IFCS), which was formed after the Rio Summit to manage these programs. This article describes some of the initiatives in capacity building that marked the development of environmental toxicology in Australia since 1992 in response to these international environmental health initiatives.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.046
GPT teacher head0.362
Teacher spread0.317 · 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.

Study designOther design
Domainnot available
GenreReview

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
Published2007
Admission routes1
Has abstractyes

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