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Record W2133370072 · doi:10.1897/04-126r.1

Hepatic retinoids of bullfrogs in relation to agricultural pesticides

2005· article· en· W2133370072 on OpenAlexaffabout
Monique Boily, Virginie E. Bérubé, Philip A. Spear, Christian Deblois, Nathalie Dassylva

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPesticideAmphibianBiologyAgricultureEcotoxicologyEcologyRetinoidToxicology

Abstract

fetched live from OpenAlex

Agricultural pesticides often have been cited as a factor affecting indigenous amphibian populations, but possible effects of pesticides and other factors associated with agricultural practices are understood poorly. Adult bullfrogs (Rana catesbeiana) were collected within the Yamaska River basin (Quebec, Canada) in subwatersheds representing low, medium, and high agricultural activities and 53 pesticides were analyzed in surface water. More pesticides were detected in subwatersheds associated with high agricultural activities like Rivière Noire and Rivière à la Barbue and pesticide concentrations were higher compared to the other study sites. Female and male body weights differed between sites. In the case of males, body weight was significantly less at Rivière à la Barbue. Liver retinol stores were decreased significantly in male bullfrogs from Rivière Noire, although total retinyl esters concentrations varied between sites having the highest concentration at Yamaska-Nord where the agricultural activity was considered low. The ratio of hepatic retinyl palmitate to retinol tended to be higher for male bullfrogs from Rivière Noire and Rivière à la Barbue. These results suggest that factors associated with intensive agricultural practices may affect the body weight and retinoid stores in male bullfrogs living in these agroecosystems.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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)0.0030.000

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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 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

Citations41
Published2005
Admission routes2
Has abstractyes

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