MétaCan
Menu
Back to cohort
Record W3163964079 · doi:10.18174/543797

Opportunities for valorisation of pelagic Sargassum in the Dutch Caribbean

2021· report· en· W3163964079 on OpenAlexaff
Ana M. López‐Contreras, Matthijs van der Geest, B. Deetman, Sander van den Burg, G.M.H. Brust, Truus de Vrije

Bibliographic record

Venuenot available
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Research and Applications
Canadian institutionsImpact
FundersMinisterie van Landbouw, Natuur en Voedselkwaliteit
KeywordsSargassumValorisationGeographySeagrassEnvironmental protectionFisheryEcologyEcosystemAlgaeBiologyEngineering

Abstract

fetched live from OpenAlex

Since 2011, unprecedented beaching events of Sargassum seaweed have caused major environmental, health and economic problems in the Caribbean, Gulf of Mexico, northern Brazil and the western coast of Africa. Not only are Sargassum influxes threatening already fragile and often endangered coastal ecosystems, such as coral reefs, mangroves and seagrass beds, they also disrupt the livelihoods of communities, especially those associated with the tourism and fishing sectors. The aim of this study is to develop a plan to turn these “brown tides” into opportunities for sustainable, scalable and efficient harvesting and valorisation approaches that will deliver environmental and socioeconomic benefits to the Dutch Caribbean and other end-users in the region. In this report we have reviewed the state-of-the-art of research on recent Sargassum blooms and influxes in the Caribbean with respect to biology, ecology, origin, distribution, socio-ecological impact and management options, in addition to existing valorisation chains and uses of Sargassum biomass, and environmental and socio-economic impacts of Sargassum valorisation. We conclude that value chains based on valorisation of nearshore Sargassum biomass into biofuel and agricultural products (i.e. fertilizer, animal feed supplement) seem the most promising for the Dutch Caribbean islands, since they will contribute to sustainable energy and food security, while reducing environmental impact of the energy and agricultural sector. Some of the identified management and valorisation strategies could also be applied to other areas that are affected by massive Sargassum influxes, such as the Gulf of Mexico. During 2020 pelagic Sargassum samples have been collected in different locations in the Caribbean region (Bonaire, Mexico, St Maarten) and Florida. These samples have been analysed for major components (including sugars and ash) and for content in iodine and heavy metals. The values obtained in these analysis were compared to values reported in the literature. The results of these analysis are of importance for the definition of possible applications of the Sargassum biomass or its products as component in the food and feed value chains Based on our literature review, a number of knowledge gaps have been identified that are related to the availability of pelagic Sargassum biomass, the environmental impacts of the harvesting, technological challenges, effects of Sargassum leachates to the environment (in case of agricultural uses) and on socio-economic impacts of the current and predicted actions (Chapter 7). These knowledge gaps need to be addressed before (commercial) harvesting and valorisation actions concerning pelagic Sargassum are taken. Therefore, we defined an implementation plan (Section 7.5 and Fig. 1), which addresses these knowledge gaps to set the first steps in the short term towards efficient and sustainable management and valorisation of pelagic Sargassum in the Dutch Caribbean. In the potential value chains defined, prevention of landings of Sargassum on the coasts is preferred to harvesting onshore. This is due to the negative ecological impact of harvesting onshore and the lower quality of beached Sargassum, that decays very rapidly.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.239
Threshold uncertainty score0.475

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.361
GPT teacher head0.362
Teacher spread0.000 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations16
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicBotanical Research and ApplicationsFrench-language works237,207