MétaCan
Menu
Back to cohort
Record W2607893369 · doi:10.1139/facets-2016-0041

Survey of mercury in boreal chorus frog (<i>Pseudacris maculata</i>) and wood frog (<i>Rana sylvatica</i>) tadpoles from wetland ponds in the Prairie Pothole Region of Canada

2017· article· en· W2607893369 on OpenAlexaffvenueabout
Stacy A. Boczulak, Mark C. Vanderwel, Britt D. Hall

Bibliographic record

VenueFACETS · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsBorealWetlandEcologyBiologyTaigaTrophic levelHylidaeAmphibian

Abstract

fetched live from OpenAlex

Tadpoles are important prey items for many aquatic organisms and often represent the largest vertebrate biomass in many fishless wetland ecosystems. Neurotoxic mercury (Hg) can, at elevated levels, decrease growth, lower survival, and cause developmental instability in amphibians. We compared total Hg (THg) body burden and concentration in boreal chorus frog ( Pseudacris maculata) and wood frog ( Rana sylvatica) tadpoles. Overall, body burden and concentration were lower in boreal chorus frog tadpoles than wood frog tadpoles, as expected, because boreal chorus frog tadpoles consume at lower trophic levels. The variables species, stage, and mass explained 21% of total variation for body burden in our models but had negligible predictive ability for THg concentration. The vast majority of the remaining variation in both body burden and THg concentration was attributable to differences among ponds; tadpoles from ponds in three areas had considerably higher THg body burden and concentration. The pond-to-pond differences were not related to any water chemistry or physical parameter measured, and we assumed that differences in wetland geomorphology likely played an important role in determining Hg levels in tadpoles. This is the first report of Hg in frog tadpoles in the Prairie Pothole Region of North America.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.465

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 studies0.0000.000
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 head0.026
GPT teacher head0.260
Teacher spread0.234 · 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

Citations9
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueFACETSSame topicMercury impact and mitigation studiesFrench-language works237,207