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SELF‐THINNING AND SIZE INEQUALITY DYNAMICS IN A CLONAL SEAWEED (<i>SARGASSUM LAPAZEANUM</i>, PHAEOPHYCEAE)<sup>1</sup>

2008· article· en· W2104070386 on OpenAlexaff
Mónica Georgina Rivera, Ricardo A. Scrosati

Bibliographic record

VenueJournal of Phycology · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsFrondBiologyHoldfastBotanyFucalesBiomass (ecology)ThinningEcologyAlgae

Abstract

fetched live from OpenAlex

Fronds of clonal seaweeds with extensive holdfasts relative to frond size are known not to self-thin during growth, even in crowded stands. We tested whether frond self-thinning would occur for such a seaweed since these traits are more similar to those of unitary seaweeds, which do self-thin in crowded conditions. We used Sargassum lapazeanum Setch. et N. L. Gardner (Fucales, Phaeophyceae) from the Pacific coast of Mexico, for which we first confirmed its clonal nature by performing a regeneration experiment in culture tanks. During the growth season (winter to late spring), S. lapazeanum stand biomass increased, while frond density and size inequality (Gini coefficient for frond biomass) decreased. These results indicate that self-thinning occurred at the frond level. We propose a conceptual model for frond dynamics for clonal seaweeds in general. In stands of clonal species with small fronds and relatively extensive holdfasts (particularly when holdfasts are perennial), frond dynamics would be determined mostly by intraclonal regulation, which seems to prevent excessive crowding from occurring. Such species display a positive biomass-density relationship during the growth season. On the contrary, in stands of clonal species with large fronds relative to holdfast size, frond dynamics would be determined mostly by interactions among genets. For such species, self-thinning may be detected at the frond level in crowded stands, resulting in a negative biomass-density relationship during growth.

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 score0.048
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.214
Teacher spread0.201 · 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

Citations19
Published2008
Admission routes1
Has abstractyes

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