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Record W2131756707 · doi:10.1139/z03-188

Growth-related life-history traits of an invasive gammarid species: evaluation with a Laird–Gompertz model

2003· article· en· W2131756707 on OpenAlexvenueno aff
Christophe Piscart, Simon Devin, Jean‐Nicolas Beisel

Bibliographic record

VenueCanadian Journal of Zoology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsGompertz functionEcologyBiologySexual maturityPopulationReproductionPopulation growthEcosystemDemography

Abstract

fetched live from OpenAlex

Although the invasive gammarid Dikerogammarus villosus (Crustacea, Amphipoda) is a recent successful invader of Western Europe's lakes and rivers and a threat to North American aquatic ecosystems, its biology is scarcely known. Different growth models for each sex were established for the first time for a natural population of a freshwater gammarid. The Laird–Gompertz growth curve was used because it best fit our data, and it was associated with an environmental forcing function to adjust the growth rate according to seasonal variations in environmental conditions. The growth curve was applied to a length decomposition obtained using the Bhattacharya method, realised on data obtained from a 1-year population dynamics study. The models allowed an assessment of biological traits such as life-span, the age of sexual maturity, the potential number of generations per year, and the growth rate as a function of environmental conditions. Differences in growth rate between males and females were consistent with biological processes such as allocation of energy for reproduction. Dikerogammarus villosus had higher rates of growth and earlier sexual maturity than all other taxa studied, which may explain its invasive tendencies and its ability to colonize numerous new ecosystems, thus becoming a cosmopolitan freshwater species.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.981

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.000

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 head0.025
GPT teacher head0.199
Teacher spread0.174 · 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

Citations39
Published2003
Admission routes1
Has abstractyes

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