MétaCan
Menu
Back to cohort

DIFFERENTIAL SNAIL PREDATION BY AN EXOTIC CRAB AND THE GEOGRAPHY OF SHELL-CLAW COVARIANCE IN THE NORTHWEST ATLANTIC

2008· article· en· W2161198860 on OpenAlexafffund
Timothy C. Edgell, Rémy Rochette

Bibliographic record

VenueEvolution · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of New Brunswick
FundersNew Brunswick Innovation Foundation
KeywordsBiologySnailClawCovariancePredationDifferential (mechanical device)EcologyStatistics

Abstract

fetched live from OpenAlex

Here we investigate if predation by the European green crab (Carcinus maenas) differs between two congeneric snails in the northwest Atlantic (Littorina littorea and L. obtusata), and ask if differential predation can help explain the geography of claw and shell forms among geographically separated populations. First, correlations between crusher-claw size and shell mass -- tested across a wide size range of animals -- were highly significant among populations of C. maenas and L. obtusata, whereas only a small number of significant correlations were found between C. maenas and L. littorea, and these were limited to the smaller size classes of snails and crabs. Moreover, among populations, L. obtusata shells were more frequently scarred than those of L. littorea, and L. obtusata were attacked and killed more frequently than L. littorea during field- and laboratory-predation experiments. Combined, results suggest L. obtusata is currently under greater selection by C. maenas than L. littorea for more crab-resistant shell forms. One possible explanation for these patterns is that L. littorea may have interacted with green crabs for centuries (in Europe) prior to their reintroduction to green crabs in America, thus predator-resistance may had already evolved.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.996

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)0.0010.000

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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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

Citations39
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueEvolutionSame topicMarine and fisheries researchFrench-language works237,207