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Record W3198404514 · doi:10.1071/mf21016

Holdfast coalescence between buoyant and non-buoyant seaweeds

2021· article· en· W3198404514 on OpenAlexaff
Eleanor R.M. Kelly, Grace Cowley, Ceridwen I. Fraser

Bibliographic record

VenueMarine and Freshwater Research · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsFraser Health
Fundersnot available
KeywordsHoldfastInterspecific competitionBiologyEcologyKelpIntraspecific competitionPaleontology

Abstract

fetched live from OpenAlex

Some inherently poorly dispersive marine species have surprisingly large or patchy distributions that might be explained by rafting or ‘hitchhiking’. The genus Durvillaea (southern bull kelp) includes both highly buoyant and entirely non-buoyant species. Several of the non-buoyant, poorly dispersive species have puzzling distributions that are hard to explain without invoking long-distance dispersal hypotheses. We propose that these non-dispersive species of Durvillaea may be able to hitchhike with buoyant, dispersive congenerics by interspecific holdfast coalescence. Although many cases of intraspecific holdfast coalescence have been recorded, interspecific coalescence is less well documented. To determine whether interspecific holdfast coalescence occurs in Durvillaea, a rock platform on the south-east coast of New Zealand was surveyed, revealing multiple examples of naturally occurring interspecific holdfast coalescence. Samples were taken from coalesced holdfasts and genetic sequencing was performed to attempt to gauge whether tissue from both species was mixed throughout the holdfast or remained discrete. The discovery of interspecific coalescence between non-buoyant and buoyant Durvillaea raises the possibility that non-buoyant seaweeds may disperse with buoyant congenerics by rafting, and could help explain the distributions of various other non-buoyant macroalgae.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.277
Teacher spread0.229 · 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.

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

Citations6
Published2021
Admission routes1
Has abstractyes

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