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Record W1986706989 · doi:10.1080/13657300903123969

SURVEY OF ETHNIC LIVE SEAFOOD MARKET OPERATORS IN THE NORTHEASTERN USA

2009· article· en· W1986706989 on OpenAlexaboutno aff
Joseph J. Myers, Ramu Govindasamy, John W. Ewart, Bin Liu, Yumin You, Venkata S. Puduri, Linda J. O’Dierno

Bibliographic record

VenueAquaculture Economics & Management · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsnot available
FundersAgricultural Marketing ServiceMassachusetts Institute of Technology
KeywordsBusinessQuarter (Canadian coin)Ethnic groupMarketingAgricultural economicsGeographyEconomics

Abstract

fetched live from OpenAlex

From February through August of 2006, a team of two researchers visited 130 ethnic live seafood markets in the northeastern USA that sell live seafood. Operators of 27% of these locations completed a survey asking basic information about their businesses with respect to live seafood. This study provides interesting baseline information on these markets directly from market managers and operators. The markets surveyed have been in business for median of nine years. Sixty-three percent receive more than one live fish shipment per week. Fifty-five percent of markets sell over 227 kg of live seafood per month. Asians are the predominant clientele in most of these locations. Most market operators stated they prefer freshness and quality over price and availability. About the same number of markets identified strong sales during the winter months as those that indicated constant live seafood sales. Live tilapia and hybrid striped bass are the two most common products. Sixty-two percent of market operators view the live seafood section as very important to overall sales in their store and roughly one-quarter of those surveyed are considering expansion.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.019
GPT teacher head0.246
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

Citations7
Published2009
Admission routes1
Has abstractyes

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