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Record W1972855066 · doi:10.1080/08927010512331344188

Variation among families for characteristics of the adhesive plaque in the barnacle<b><i>Balanus amphitrite</i></b>

2005· article· en· W1972855066 on OpenAlexfundno aff
Eric Holm, Beatriz Orihuela, Christopher Kavanagh, Daniel Rittschof

Bibliographic record

VenueBiofouling · 2005
Typearticle
Languageen
FieldEngineering
TopicMarine Biology and Environmental Chemistry
Canadian institutionsnot available
FundersOffice of Naval ResearchMcMaster University
KeywordsBarnacleBalanusSilasticAdhesiveThoracicaSiliconeCoatingComposite materialMaterials scienceBiologyZoologyCrustacean

Abstract

fetched live from OpenAlex

A quantitative genetics approach was used to examine variation in the characteristics of the adhesive plaque of the barnacle Balanus amphitrite Darwin attached to two silicone substrata. Barnacles settled on silicone polymer films occasionally form thick, soft adhesive plaques, in contrast to the thin, hard plaques characteristic of attachment to other surfaces. The proportion of barnacles producing a thick adhesive plaque was 0.31 for Veridian, a commercially available silicone fouling-release coating, and 0.18 for Silastic T-2, a silicone rubber used for mold-making. For both materials, significant variation among maternal families in the proportion of barnacles producing a thick adhesive plaque was observed, which suggests the presence of genetic variation, or maternal environmental effects, for this plaque characteristic. For the Veridian coating, barnacles expressing the thick adhesive plaque also exhibited significantly reduced tenacity. This represents the first reported case for potential genetic control of intraspecific phenotypic variation in the physical characteristics and tenacity of the adhesive of a fouling invertebrate.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.171
Teacher spread0.166 · 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

Citations54
Published2005
Admission routes1
Has abstractyes

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