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Record W2536371091

Mussel: Festivals and producers

2000· article· en· W2536371091 on OpenAlexaboutno aff
Eric G. Gasataya

Bibliographic record

VenueAquaDocs (United Nations Educational, Scientific and Cultural Organization) · 2000
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsnot available
Fundersnot available
KeywordsMusselGeographyBusinessFisheryBiology
DOInot available

Abstract

fetched live from OpenAlex

Mussel: festivals and producersA mussel farmer in the Philippines stands by his harvest next page By E Gasataya Since ancient times, mussels have been gathered from the wild for food.There had been tales regarding mussel eating, and one of the earliest is from the west coast of America 2,400 years ago when the inhabitants turned to mussels because they had eaten so many abalone that the colonies were almost wiped out.All through the centuries man has learned to raise and harvest mussels in different ways.Cultured mussels can be harvested all year round, while mussel fisheries is defined by season.The start of a new season always calls for a celebration.The Dutch celebrates the most popular festival each year in mid-July in Yerseke, the country's "mussel capital."All the major operators attend the event that attracts attention from the world's media.The harvest is loaded onto lorries which are lined up behind an enormous banner depicting bowls of mussels.In Menai Strait in North Wales, there is also a mussel festival.This is supported by a Belgian mussel-and-chips chain whose chefs cook nearly 3,000 mussels.This event comprises of parades and races, mussel cooking competition and demonstrations, fairs and exhibits, and many more.In Bantry Bay, Ireland, an annual mussel festival is also held, and is supported by the individual operators and the Irish Sea Fisheries Board.The event includes jazz festival with non-stop music, mussel eating competitions, seafood stalls, helicopter trips around the bay, boat trips and a gala seafood banquet.Other countries have also their own mussel festival like in Europe such as Italy, France and Spain and in the other side of Atlantic in Nova Scotia and Canada.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.111
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.001
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1110.043

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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 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

Citations0
Published2000
Admission routes1
Has abstractyes

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