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Record W2474395852 · doi:10.1093/icb/icw006

Ecology and Evolution of Phenotypic Plasticity in the Penis and Cirri of Barnacles

2016· article· en· W2474395852 on OpenAlexaff
J. Matthew Hoch, Daniel T. Schneck, Christopher J. Neufeld

Bibliographic record

VenueIntegrative and Comparative Biology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsBiologyPenisMatingPhenotypic plasticityHermaphroditeSetaEcologyZoologySpermBarnacleSperm competitionAnatomyCrustaceanBotany

Abstract

fetched live from OpenAlex

Most barnacles are sessile, simultaneous hermaphrodites that reproduce by copulation. This is achieved through the extension of a muscular penis, famous for being the proportionally largest in the animal kingdom. The penis is a long cylindrical or conical organ, composed of a series of folded rings, allowing it to stretch to great lengths. The penises are covered with chemosensory setae allowing them to seek out receptive neighbors. For many species, the condition of the penis changes seasonally. In the most extreme circumstances, it degenerates and is shed during the first post-mating molt and is re-grown for the next mating season. Barnacle penises have been shown to exhibit phenotypic plasticity in response to many different challenges. When exposed to heavy waves, diameter is increased by thickening both the cuticle and muscles. When mates are far, length increases by adding ringed annulations. Experiments have shown that these plastic traits are modular, capable of changing independently from each other and that they improve mating ability. Alternate strategies to increase reproductive ability by barnacles include the production of dwarf and complemental males, sperm casting and sperm leakage, and aerial copulation. All of these mating strategies may have important implications for the study of reproductive biology, life history, and sex allocation theory.

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 score0.034
Threshold uncertainty score0.982

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)0.0000.000

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 head0.243
Teacher spread0.215 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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