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Record W2144677602 · doi:10.1080/00288330909509979

Competition between invasive green crab <i>(Carcinus maenas)</i> and American lobster <i>(Homarus americanus)</i>

2009· article· en· W2144677602 on OpenAlexafffundabout
P J Williams, C. MacSween, Melanie A. Rossong

Bibliographic record

VenueNew Zealand Journal of Marine and Freshwater Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsUniversity of New BrunswickMemorial University of NewfoundlandSaint John Regional HospitalSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of CanadaSt. Francis Xavier University
KeywordsHomarusCarcinus maenasAmerican lobsterCarapaceFisheryDecapodaBiologyCrustaceanJuvenileBayEcologyOceanography

Abstract

fetched live from OpenAlex

Abstract Green crab (Carcinus maenas) have recently invaded the southern Gulf of St. Lawrence, Canada. Although these invasive crabs have coexisted without appreciable impact on American lobster (Homarus americanus) populations in northeastern United States, Bay of Fundy, and the southern shore of Nova Scotia, Canada, the green crab in the southern Gulf of St. Lawrence likely represents a new introduction of crab from northern Europe. The southern gulf is ideal habitat for green crab, and the crab are commonly captured subtidally, increasing the potential for overlap with juvenile lobsters. To complement previous experiments with green crab versus small (28–57 mm carapace length, CL) and medium (55–70 mm CL) sized lobsters in food competition trials, this paper reports findings from trials with large (72–80 mm CL) lobsters. The lobsters were first to gain possession of the food in an equal number of trials as green crab. The large lobsters fed for 62% of the total feeding time, versus 38% for the green crab, and initiated a significantly greater number of aggressive interactions than green crab. It appears that a reversal of dominance with adult green crab occurs once lobsters exceed 72 mm CL.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.836

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 integrity0.0000.001
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 head0.026
GPT teacher head0.282
Teacher spread0.256 · 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

Citations18
Published2009
Admission routes3
Has abstractyes

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