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Record W4382725427 · doi:10.1007/s10811-023-03028-6

Morphology of Ascophyllum nodosum in relation to commercial harvesting in New Brunswick, Canada

2023· article· en· W4382725427 on OpenAlexaffabout
Jean‐Sébastien Lauzon‐Guay, Alison I. Feibel, Bryan L. Morse, R. Ugarte

Bibliographic record

VenueJournal of Applied Phycology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsAcadian Seaplants (Canada)
Fundersnot available
KeywordsAscophyllumIntertidal zoneBiologyAlgaeBiomass (ecology)ShootBotanyHabitatCanopyEcologyBrown seaweed

Abstract

fetched live from OpenAlex

Abstract Intertidal seaweed beds form three-dimensional structures providing habitat for a variety of species. As such, ecosystem-based management of seaweed harvesting must take into consideration the impact of the harvest not only on the biomass but also on the morphology of the seaweed. We compare the morphology and vertical distribution of biomass and shoots in Ascophyllum nodosum from three sites with a 20 + year history of commercial harvesting with three corresponding control sites in southern New Brunswick, Canada. We found no significant impact of harvest history on the vertical distribution of shoots or biomass within individual clumps. At two of the three harvested sites, large clumps had a wider circumference than those at the control sites, suggesting that long-term harvesting increases the growth of shoots throughout the clumps; presumably caused by an increase in light penetration through the harvested canopy. We also compare biomass of littorinids, the most abundant invertebrates found in A. nodosum beds at low tide and found no significant difference between control and harvested sites. We conclude that the harvest of A. nodosum according to the current regulations in New Brunswick, does not have long-term impact on the morphology of the algae or on the abundance of its main inhabitant.

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 score0.171
Threshold uncertainty score0.478

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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

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