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Community‐wide character displacement in barnacles: a new perspective for past observations

2003· article· en· W1513974321 on OpenAlexaffabout
Kerry B. Marchinko, Michael T. Nishizaki, Kevin C. Burns

Bibliographic record

VenueEcology Letters · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsBamfield Marine Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsBarnacleCharacter displacementInterspecific competitionCompetition (biology)Intertidal zoneEcologyCharacter (mathematics)BiologyGeographyDisplacement (psychology)Null modelCommunity structureHabitatCrustaceanGeometrySympatry

Abstract

fetched live from OpenAlex

Abstract We tested for community‐wide character displacement of feeding leg length and shell morphology in two barnacle communities on the west coast of North America (southern California, USA and Vancouver Island, Canada). Neither community exhibited even displacement in shell morphology. Both barnacle communities, however, exhibited remarkably evenly displaced feeding leg length, despite large differences in geography and species composition (between the orders Pedunculata and Sessilia). Previous experiments suggest that this pattern results from competition, although the competitive mechanism remains unknown. Displacement of leg length may reflect dietary specialization, spatial competition, or both. In some cases the results from two null models differed, illustrating the importance of employing a null model that considers mean and variance, rather than character means alone. Overall, the observed pattern of character displacement provides a new perspective for re‐examining the complex relationship between morphology and interspecific competition among intertidal barnacles.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.028
GPT teacher head0.225
Teacher spread0.197 · 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 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

Citations30
Published2003
Admission routes2
Has abstractyes

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