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A developmental bottleneck in dispersing larvae: implications for spatial population dynamics

2003· article· en· W2165628038 on OpenAlexaff
Daniel Schneider, James A. Stoeckel, Chris R. Rehmann, K. Douglas Blodgett, Richard E. Sparks, Dianna K. Padilla

Bibliographic record

VenueEcology Letters · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsZebra musselDreissenaBiological dispersalBiologyPopulationEcologyLarvaMusselBivalviaMollusca

Abstract

fetched live from OpenAlex

Abstract We found evidence for a critical population bottleneck at a developmental‐stage transition in larvae of the zebra mussel Dreissena polymorpha Pallas from field estimates of mortality. Identification of this critical period in the field was made possible by closely tracking cohorts of larvae over 5 days of development as they dispersed 128 km in a river system. The presence of a survival bottleneck during development was confirmed in laboratory studies of zebra mussel larvae. Development‐specific mortality has important implications for spatial population dynamics of the zebra mussel in particular, and all species with indirect development in general. Marine reserves that do not take development‐specific mortality into account may dramatically underestimate reserve size needed to protect rare and/or exploited marine populations. Conversely, for the zebra mussel, the lower contribution of dispersing individuals to population growth downstream of reserves can lead to more feasible control through the blocking of dispersal.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations63
Published2003
Admission routes1
Has abstractyes

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