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Record W4399491812 · doi:10.3390/foods13121810

Morzeddhu: A Unique Example of a Traditional and Sustainable Typical Dish from Catanzaro

2024· article· en· W4399491812 on OpenAlexaboutno aff
Stefano Alcaro, Roberta Rocca, Maria Grazia Rotundo, Francesco Bianco, Luigi Scordamaglia

Bibliographic record

VenueFoods · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
Fundersnot available
KeywordsPeasantQuarter (Canadian coin)Mediterranean climateQuality (philosophy)GeographyMathematicsBiologyEcology

Abstract

fetched live from OpenAlex

“Morzeddhu” in the local dialect of Catanzaro (“Morzello” in Italian) is an official typical dish of the capital of the Calabria region. It is a peasant dish, almost unknown at an international level, that labels, in an extraordinary way, the culinary identity of Catanzaro, a city founded around the X century. After America’s discovery, its preparation was optimized and definitively fixed. Its recipe is strictly based on a cow’s “fifth quarter” combined with spicy and typical Mediterranean vegetables. Remarkably, no pork meat is used, and when all traditional ingredients are included in the complex and quite long preparation of this special dish, it can deserve the title of “Illustrissimo”. This review provides a scientific description of Illustrissimo, emphasizing its unique properties and connection to the circular economy, food security, and the Mediterranean diet. We also highlight its unique quality compared to other alternatives through an analysis of their nutritional facts and bioactive compounds. Nutritionally, offal and fifth quarter components are a rich source of high-quality protein, with lower levels of total fat and saturated fatty acids compared to other meat cuts. In essence, this dish offers a great example of a high-quality yet affordable meal, aligning perfectly with a Mediterranean diet.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.643

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 head0.072
GPT teacher head0.254
Teacher spread0.183 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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