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Record W4403583064 · doi:10.1111/eff.12823

Effect of Temperature on Black Carp ( <i>Mylopharyngodon piceus</i> ) Age at Sexual Maturity

2024· article· en· W4403583064 on OpenAlexafffund
Jiayi Wu, Kim Cuddington

Bibliographic record

VenueEcology Of Freshwater Fish · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSexual maturityBiologyEcologyZoologyMaturity (psychological)Animal science

Abstract

fetched live from OpenAlex

ABSTRACT Black Carp ( Mylopharyngodon piceus ) is one of the four invasive Asian Carp species (Bighead Carp, Hypophthalmichthys nobilis ; Grass Carp, Ctenopharyngodon idella ; and Silver Carp, Hypophthalmichthys molitrix ) in North America. As benthic predators, Black Carp poses ecological risks to unionids within the Great Lakes and could bring other changes to local benthic communities. One means of assessing invasion risk and potential impact is to determine how critical life history characteristics depend on environmental conditions in new regions. Age at sexual maturity is negatively correlated to the maximum per capita population growth rate and thus can be used to estimate potential population growth for invasive species. Previous studies have found a significant negative relationship between age at maturity and temperature for all three other Asian Carp species, but not for Black Carp. With a more comprehensive dataset, we demonstrate that the same relationship exists for Black Carp. Winter duration is the best predictor of Black Carp age at maturity, followed by average winter air temperature. This significant relationship between age at maturity and temperature was not altered in artificial growing environments. Our results suggested that for Black Carp, temperatures in the winter months are more important for its energy accumulation and maturation and may determine invasion risk in northern locations, such as tributaries of the Great Lakes.

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 score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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