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Record W4386802418 · doi:10.1007/s00227-023-04289-y

Genus-specific response of kelp photosynthetic pigments to decomposition

2023· article· en· W4386802418 on OpenAlexfundno aff
Luka Seamus Wright, Louise Kregting

Bibliographic record

VenueMarine Biology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsnot available
FundersQueen's UniversityUniversity of Western Australia
KeywordsKelpBiologyDetritusKelp forestLaminaria digitataBotanyLaminariaGenusHoldfastAlgaePhotosynthesisEcology

Abstract

fetched live from OpenAlex

Abstract Detritus is widely considered to be dead organic matter. However, recent studies have shown that kelp detritus can be functionally equivalent to the attached plant. This is significant, because detritus makes up ~ 42% of all plant biomass. In the Northeast Atlantic, studies on detrital photobiology have been restricted to the genus Laminaria . Here, we present data from a 46 d in situ experiment investigating the effect of decomposition on detrital photosynthetic pigment concentrations and stoichiometry in four Northeast Atlantic kelp species from three genera. We corroborate the lack of a decomposition effect on pigments of cold temperate Laminaria species as identified by previous studies but show that the photophysiology of other kelp genera responds differently. While Saccharina latissima displays an idiosyncratic parabolic response, the annual and thermally plastic kelp Saccorhiza polyschides seems to become less pigmented with increasing detrital age. If S. polyschides becomes increasingly dominant in future parts of a warmer Northeast Atlantic, this may translate to lower detrital photosynthetic potential at the forest scale. However, given our somewhat ambiguous data on this species, this conjecture requires further investigation into detrital photosynthesis.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.018
GPT teacher head0.247
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; both teacher heads agree on what is shown here.

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

Citations4
Published2023
Admission routes1
Has abstractyes

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