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Record W1967964916 · doi:10.1890/130298

Linking human well‐being and jellyfish: ecosystem services, impacts, and societal responses

2014· review· en· W1967964916 on OpenAlexaff
William M. Graham, Stefan Gelcich, Kelly L. Robinson, Carlos M. Duarte, Lucas Brotz, Jennifer E. Purcell, Laurence P. Madin, Hermes Mianzán, Kelly R. Sutherland, Shin-ichi Uye, Kylie A. Pitt, Cathy H. Lucas, Molly Bøgeberg, Richard D. Brodeur, Robert H. Condon

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2014
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation
KeywordsJellyfishEcosystem servicesProvisioningEcosystemEnvironmental resource managementContext (archaeology)BusinessEcologyGeographyBiologyEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

Jellyfish are usually perceived as harmful to humans and are seen as “pests”. This negative perception has hindered knowledge regarding their value in terms of ecosystem services. As humans increasingly modify and interact with coastal ecosystems, it is important to evaluate the benefits and costs of jellyfish, given that jellyfish bloom size, frequency, duration, and extent are apparently increasing in some regions of the world. Here we explore those benefits and costs as categorized by regulating, supporting, cultural, and provisioning ecosystem services. A geographical perspective of human vulnerability to jellyfish over four categories of human well‐being (health care, food, energy, and freshwater production) is also discussed in the context of thresholds and trade‐offs to enable social adaptation. Whereas beneficial services provided by jellyfish likely scale linearly with biomass (perhaps peaking at a saturation point), non‐linear thresholds exist for negative impacts to ecosystem services. We suggest that costly adaptive strategies will outpace the beneficial services if jellyfish populations continue to increase in the future.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.814
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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
GenreReview

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

Citations169
Published2014
Admission routes1
Has abstractyes

Explore more

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