MétaCan
Menu
Back to cohort
Record W2972970241 · doi:10.1080/00318884.2019.1640996

Development of seaweed cultivation in Latin America: current trends and future prospects

2019· article· en· W2972970241 on OpenAlexaboutno aff
Alejandro Espi Alemañ, Daniel Robledo, Leila Hayashi

Bibliographic record

VenuePhycologia · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsnot available
Fundersnot available
KeywordsAquacultureTemperate climateAgricultureLivelihoodDiversification (marketing strategy)TropicsBiologyGeographyFisheryAgroforestryEcologyBusinessFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Seaweed farming has now expanded across several continents from South East Asia to South America, Northern Europe, Canada and East Africa, contributing to global food security, supporting rural livelihoods, alleviating poverty and improving the health of oceans. Latin America (LA) covers a vast geographical area, which includes four different oceanic domains (Temperate Northern Pacific, Tropical Eastern Pacific, Temperate South America and Tropical Atlantic) and encompasses many types of coastal ecosystems with a wide range of seaweed species. LA has major potential for the development of seaweed farming activities; however, almost all the production is based on the harvesting of natural beds. This review describes the development of and prospects for the aquaculture seaweed industry in LA. The status of the seaweed aquaculture sector for green, brown and red seaweed and the main industry challenges are addressed. Regulation in the primary countries is also discussed. The expansion of the aquaculture industry in this region can be improved with new strains and farming methodologies, diversification of species, market expansion and an increase in domestic demand.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.889

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)0.0010.000

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 head0.013
GPT teacher head0.221
Teacher spread0.207 · 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

Citations84
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePhycologiaSame topicMarine and coastal plant biologyFrench-language works237,207