MétaCan
Menu
Back to cohort
Record W4408444274 · doi:10.1007/s10750-024-05783-0

Insights into the feeding of jellyfish polyps in wild and laboratory conditions: do experiments provide realistic estimates of natural functional rates?

2025· article· en· W4408444274 on OpenAlexfundno aff
Cathy H. Lucas, Danja P. Hoehn, Clive N. Trueman

Bibliographic record

VenueHydrobiologia · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsnot available
FundersNatural Environment Research CouncilMcGill University
KeywordsJellyfishBiologyEcologyFunctional responseNatural (archaeology)Fish <Actinopterygii>ZoologyFisheryPredationPredator

Abstract

fetched live from OpenAlex

Abstract Biotic and abiotic factors that affect the physiology and ecology of scyphozoan polyps are considered to be major drivers of jellyfish blooms, but are rarely studied under field conditions. Here, stable isotopes of carbon and nitrogen were used to investigate feeding ecology in Aurelia aurita polyps from the Beaulieu River, UK (50° 80′ 04.55″ N/1° 42′ 28.12″ W) in both winter and summer conditions, and compared to laboratory-maintained polyps fed Artemia nauplii at 6 and 20 °C, respectively. In natural conditions, the isotopic composition of A. aurita polyps indicated assimilation of nutrients derived from both benthic and pelagic food pathways, with seasonal switches between benthic-derived nutrients in winter and pelagic-derived nutrients in summer. In laboratory experiments, polyps assimilated Artemia food at 6 °C although metabolic processes were reduced, while at 20 °C, polyps starved as their increased metabolic costs could not be met from the Artemia food. Experiments on growth and asexual reproduction of Artemia -fed polyps of A. aurita may not reflect natural metabolic rates especially at higher temperatures (e.g. 20 °C), because these polyps are not extracting sufficient resources from their Artemia food to fuel the increased metabolic costs associated with high temperatures.

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 score0.020
Threshold uncertainty score0.230

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)0.0000.000

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 head0.009
GPT teacher head0.237
Teacher spread0.227 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueHydrobiologiaSame topicMarine Invertebrate Physiology and EcologyFrench-language works237,207