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Record W2168703869 · doi:10.1897/05-622r.1

Dietary exposure to low pesticide doses causes long-term immunosuppression in the leopard frog (<i>Rana pipiens</i>)

2007· article· en· W2168703869 on OpenAlexaff
A. Albert, Ken G. Drouillard, G. Douglas Haffner, Brian Dixon

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsUniversity of WindsorUniversity of Waterloo
Fundersnot available
KeywordsLeopard frogDieldrinPesticideBiologyImmunosuppressionToxicityImmune systemRanaToxicologyInternal medicineEndocrinologyImmunologyEcologyMedicine

Abstract

fetched live from OpenAlex

This study examines the relationship between dietary exposure of pesticides, DDT, and dieldrin and immunosuppression in the northern leopard frog (Rana pipiens). Immune function was measured before, during, and after a 10-week exposure period with the use of both adaptive and innate immunity responses. Exposure to low doses (75 ng/g body wt DDT or 2.1 ng/g dieldrin total dose over the 10 weeks) resulted in significant suppressive effects on antibody production and secondary delayed-type hypersensitivity (DTH). The high doses (750 ng/g DDT and 21 ng/g dieldrin), however, did not affect antibody production, DTH, or oxidative burst in a predictable dose-response manner. The differences in magnitude and direction of the effects of the two dosing regimes were likely due to differences in chemical exposure on the basis of feeding and effectiveness of chemical uptake. The low dose results demonstrated that moderate concentrations of pesticides, frequently observed in the environment, are able to weaken the immune response of R. pipiens.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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 designBench or experimental
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

Citations32
Published2007
Admission routes1
Has abstractyes

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