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Record W4409696634 · doi:10.1111/jne.70027

Effects of wintering under methylmercury exposure on spring reproductive onset in song sparrows ( <i>Melospiza melodia</i> )

2025· article· en· W4409696634 on OpenAlexafffund
Claire Bottini, Scott A. MacDougall‐Shackleton

Bibliographic record

VenueJournal of Neuroendocrinology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaBritish Society for Neuroendocrinology
KeywordsMethylmercuryInternal medicineEndocrinologyBiologyMedicineEcology

Abstract

fetched live from OpenAlex

Exposure to methylmercury (MeHg) on breeding grounds may have numerous deleterious effects on birds, including neurotoxicity, disruption of hormones, and impaired reproduction. But it is unknown if MeHg exposure on wintering grounds can carry over and produce negative effects on the following spring breeding seasonal transition. To evaluate this, we exposed male captive song sparrows (Melospiza melodia) to environmentally relevant levels of dietary MeHg for 3 months during winter. We then photostimulated the birds with a long-day photoperiod and observed them for 21 days post-exposure. Contrary to our predictions, we found no carry-over effects of MeHg on the timing of changes in spring reproductive physiology assessed by testes mass, syrinx mass, plasma androgen concentrations, number of GnRH neurosecretory cells, and body condition. However, following photostimulation, MeHg-exposed birds had smaller cloacal protuberances. Although we observed no obvious effects on the timing of reproductive onset, the results suggest that winter MeHg exposure could induce carry-over effects on secondary sexual traits that may affect birds' breeding performance. Overall, our findings indicate that songbirds can buffer against the main effects of prior winter MeHg exposure so as to not delay reproductive onset in spring, but more studies are required for long-term effects on breeding performance.

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 score0.264
Threshold uncertainty score0.540

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)0.0000.000

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 head0.012
GPT teacher head0.270
Teacher spread0.257 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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