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Record W2111840549 · doi:10.1139/z04-125

Laboratory culture of<i>Dreissena polymorpha</i>larvae: spawning success, adult fecundity, and larval mortality patterns

2004· article· en· W2111840549 on OpenAlexvenueno aff
James A. Stoeckel, Dianna K. Padilla, Daniel Schneider, Chris R. Rehmann

Bibliographic record

VenueCanadian Journal of Zoology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyFecundityLarvaDreissenaBroodAvian clutch sizeZoologyPopulationEcologyReproductionBivalviaMolluscaDemography

Abstract

fetched live from OpenAlex

Understanding the entire life history, especially critical periods during the life cycle of a species, is important for understanding population dynamics and is crucial for control of nuisance species. Errors in estimates of growth rates and recruitment in field and modeling studies are likely to occur if one assumes uniform rather than size- or stage-specific mortalities for larval stages. Similarly, effects of brood-stock health on adult fecundity and larval mortality patterns might affect results of laboratory studies investigating larval life history. We maintained zebra mussels, Dreissena polymorpha (Pallas, 1771), in spawning condition in the laboratory for 24 weeks. Healthy larvae were produced for 18 weeks. However, induction of spawning and egg production declined through time. Fecundity increased with size per female, but sperm production did not for males. We found no correlation between clutch size and egg size across a wide range of clutch sizes. Survivorship was greater when larvae were reared at lower densities. Daily larval mortality was strongly size dependent; the average size of dead larvae was similar to the minimum size of live larvae. Highest daily larval mortality occurred during the transition from D stage to umbonal stage, supporting the suggestion of a developmental bottleneck as found in previous field studies.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.999

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)0.0020.000

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 head0.220
Teacher spread0.211 · 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

Citations47
Published2004
Admission routes1
Has abstractyes

Explore more

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