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Record W4402442418 · doi:10.1007/s10811-024-03347-2

Under pressure: inhibited sporophyte growth of the sugar kelp Saccharina latissima (Phaeophyceae)

2024· article· en· W4402442418 on OpenAlexfundno aff
Eoghan M. Cunningham, Luka Seamus Wright, Molly Crowe, E. G. Healey, Laura F. Robinson, Hong Chin Ng, Louise Kregting

Bibliographic record

VenueJournal of Applied Phycology · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsnot available
FundersNatural Environment Research CouncilUniversity of AberdeenQueen's UniversityDepartment of Agriculture, Environment and Rural Affairs, UK GovernmentQueen's University BelfastDepartment for Energy Security and Net Zero
KeywordsKelpSporophyteAquacultureHatcheryBiologyAlgaeFisheryBotanyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract Seaweed aquaculture, which takes place mostly in Asia, is a lucrative industry that is valued &gt; US $9 billion. However, technological modifications are needed to ensure economic viability and growth of the seaweed aquaculture industry throughout Europe. While current research is investigating the use of certain mechanised processes in seaweed aquaculture, the impact of pressurised spraying of macroalgal cultures on subsequent growth remains unknown. Here, we aimed to determine the efficacy of a future mechanised seeding procedure by investigating how differing pressure treatments impact upon the growth and percentage cover of zoospores seeded onto twine in the hatchery, using the kelp Saccharina latissima as a model species. Zoospore solutions were subjected to pressures of 1, 2, 3, 4, and 5 bar, before being seeded on hatchery twine and left to grow for 7 weeks. We demonstrate that both percentage cover and sporophyte lengths for S. latissima are significantly reduced by ~ 22% and ~ 61%, respectively, when juvenile zoospores are subjected to increasing pressure from 1 to 5 bar. This indicates that minimal pressure in the use of mechanised hatchery techniques is optimal for growth of seaweed.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
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)0.0020.000

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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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