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Record W2097706356 · doi:10.1002/etc.382

Species extrapolation for the 21st century

2010· article· en· W2097706356 on OpenAlexaff
Malin Celander, Jared V. Goldstone, Nancy D. Denslow, Taisen Iguchi, Peter Kille, Roger D. Meyerhoff, Ben A. Smith, Thomas H. Hutchinson, James R. Wheeler

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsStantec (Canada)
FundersNatural Environment Research CouncilU.S. Environmental Protection Agency
KeywordsExtrapolationEnvironmental scienceBiologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Safety factors are used in ecological risk assessments to extrapolate from the toxic responses of laboratory test species to all species representing that group in the environment. More accurate extrapolation of species responses is important. Advances in understanding the mechanistic basis for toxicological responses and identifying molecular response pathways can provide a basis for extrapolation across species and, in part, an explanation for the variability in whole organism responses to toxicants. We highlight potential short- and medium-term development goals to meet our long-term aspiration of truly predictive in silico extrapolation across wildlife species' response to toxicants. A conceptual approach for considering cross-species extrapolation is presented. Critical information is required to establish evidence-based species extrapolation, including identification of critical molecular pathways and regulatory networks that are linked to the biological mode of action and species' homologies. A case study is presented that examines steroidogenesis inhibition in fish after exposure to fadrozole or prochloraz. Similar effects for each compound among fathead minnow, medaka, and zebrafish were attributed to similar inhibitor pharmacokinetic/pharmacodynamic distributions and sequences of cytochrome P45019A1/2 (CYP19A1/2). Rapid advances in homology modeling allow the prediction of interactions of chemicals with enzymes, for example, CYP19 aromatase, which would eventually allow a prediction of potential aromatase toxicity of new compounds across a range of species. Eventually, predictive models will be developed to extrapolate across species, although substantial research is still required. Knowledge gaps requiring research include defining differences in life histories (e.g., reproductive strategies), understanding tissue-specific gene expression, and defining the role of metabolism on toxic responses and how these collectively affect the power of interspecies extrapolation methods.

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)0.015
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.034
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0020.004
Scholarly communication0.0040.010
Open science0.0040.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0180.005

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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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 designTheoretical or conceptual
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

Citations66
Published2010
Admission routes1
Has abstractyes

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