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Record W2551564522 · doi:10.1139/cjfas-2016-0324

Interactive effects of road salt and leaf litter on wood frog sex ratios and sexual size dimorphism

2016· article· en· W2551564522 on OpenAlexvenueno aff
Max R. Lambert, Aaron B. Stoler, Meredith S. Smylie, Rick A. Relyea, David K. Skelly

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
FundersYale University
KeywordsBiologyPlant litterEcologyLitterSexual dimorphismTree frogLithobatesPopulationAmphibianZoologyEcosystem

Abstract

fetched live from OpenAlex

Myriad natural and anthropogenic chemicals alter aquatic vertebrate sex ratios, with implications for population dynamics. Despite 22 million metric tons of salt applied to US roads annually, with much of it entering aquatic environments, it is unknown whether salt impacts sex ratios. Moreover, changes in forest composition co-occur with increased road salt application, dramatically changing ecosystems. We explore how road salt (sodium chloride) and two leaf litter types might influence amphibian development. By examining wood frog (Rana sylvatica = Lithobates sylvaticus) metamorphs reared with different combinations of salt (114 and 867 mg Cl·L −1 ) and litter species (none, maple (Acer rubrum), oak (Quercus spp.)), we show that salt masculinizes tadpole sex ratios, whereas oak, but not maple, litter feminizes populations. Road salt addition eliminates sexual dimorphism in oak-reared tadpoles, but enhances sexual size dimorphisms in maple-reared tadpoles, producing larger females. We are the first to show that road salt and native tree leaf litter manipulates vertebrate sex ratios and sex-specific development. Human land use might therefore influence vertebrate development through direct effects of contamination and indirect effects of altered botanical composition.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.560

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 studies0.0000.001
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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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

Citations41
Published2016
Admission routes1
Has abstractyes

Explore more

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