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Record W4389950200 · doi:10.3389/fmars.2023.1304000

Macroalgal features and their influence on associated biodiversity: implications for conservation and restoration

2023· article· en· W4389950200 on OpenAlexafffund
Emily G. Gibbons, Pedro A. Quijón

Bibliographic record

VenueFrontiers in Marine Science · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Prince Edward Island
KeywordsBiodiversityEcologyHabitatClimate changeAbundance (ecology)Habitat destructionBiologyPopulationGeographyInvertebrate

Abstract

fetched live from OpenAlex

Studies examining the relationship between seaweeds and the diversity of associated organisms have been attempted far less than those focusing on the influence of terrestrial plants. That is troublesome considering the growing number of studies reporting the decline or local loss of macroalgae because of ocean warming and climate change. Since the fate of macroalgae will have an influence on associated organisms, this brief overview examined the different roles played by macroalgae, making the distinction between morphological features associated with individual seaweed species and those associated with populations or habitats. Most studies at both (individual and population) levels have identified positive relationships between morphological features such as structural complexity (including fractal dimensions) and invertebrate biodiversity, and the abundance of various faunistic groups. Some of these relationships are stronger than others, often with complex outcomes, suggesting that the current and future ecological benefits provided by macroalgae are strongly species- and habitat-dependent. While the displacement or local-scale loss of seaweeds may continue because of climate change, the features identified here may become useful in light of conservation and restoration efforts.

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 score0.083
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.213
Teacher spread0.200 · 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

Citations30
Published2023
Admission routes2
Has abstractyes

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