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Record W269938600

Will the introduced mussel Mytilus galloprovincialis outcompete the native mussel M. trossulus in Puget Sound? A study of relative survival and growth rates among different habitats

2005· article· en· W269938600 on OpenAlexvenueno aff
Joel K. Elliott, Michelle A. Rensel, Peter Wimberger

Bibliographic record

VenueSound Ideas (University of Puget Sound) · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsnot available
Fundersnot available
KeywordsMusselMytilusFisherySound (geography)HabitatBiologyBlue musselInvasive speciesEcologyOceanographyGeology
DOInot available

Abstract

fetched live from OpenAlex

The Mediterranean mussel (Mytilus galloprovincialis = Mg) has become established throughout Puget Sound and the effects of this species introduction on the native mussel M. trossulus (Mt) are unknown. Mg and hybrids between Mg and Mt (Mgt) are abundant on floating docks and large Mg and Mgt mussels also occur at low frequencies in the intertidal zone and in areas of low salinity. A variety of factors (e.g., growth/survival rates, predation, larval recruitment) may be causing these observed distribution patterns. In this study we performed three field experiments to examine the relative survival and growth rates of Mg and Mt: 1) at different tide heights on pilings vs subtidal locations on docks, 2) when grown in single-species groups and mixed-species groups, and 3) at locations with different salinities. Individuals of Mg had higher survival and growth than Mt in all areas except the high intertidal. Each species had similar growth rates when grown in single and mixed-species groups. Mg had higher survival and growth rates than Mt under both high and low salinity. These results indicate that the introduced Mg has superior growth and survival to the native Mt, and Mg has the potential to outcompete Mt under certain environmental conditions.

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)0.001
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 score0.274
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

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