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Record W2995431017 · doi:10.1186/s42779-019-0025-3

An endangered regional cuisine in Sweden: the decline in use of European smelt, Osmerus eperlanus (L., 1758), as food stuff

2019· article· en· W2995431017 on OpenAlexfundno aff
Ingvar Svanberg, Armas Jäppinen, Madeleine Bonow

Bibliographic record

VenueJournal of Ethnic Foods · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsSmeltFishingFisheryGeographyEndangered speciesSubsistence agricultureFish <Actinopterygii>Commercial fishingEcologyAgricultureArchaeologyBiology

Abstract

fetched live from OpenAlex

Abstract Only a handful freshwater fish species are still commercially sought after in Sweden. Subsistence fishing in lakes and rivers is also rare nowadays and has in general been replaced by recreational fishing. However, fishing for European smelt, Osmerus eperlanus (L.), once popular in many areas of central Sweden, has survived into the twenty-first century, particularly in the province of Värmland, as a minor, but interesting regional food speciality. It is a dish with character, since smelt has a very particular scent and it is therefore esteemed by some and rejected by others. Nowadays, it is eaten locally, especially by the elder generations, and attempts to popularize it as a regional food have so far failed. However, smelt deserves to be marketed as a regional culinary specialty, and has great potential to become popular among modern foodies. A traditional dish known as “smelt pancake” can be promoted. Interesting enough, there are new categories of smelt enthusiasts that have discovered the possibility of fishing in large numbers in spring, especially Thai and other immigrants. There are also a significant numbers of sojourners and visitors from the Baltic States, especially Lithuanians, fishing for smelt in Värmland.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.197

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 science0.0000.000
Research integrity0.0000.000
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 head0.093
GPT teacher head0.304
Teacher spread0.211 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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