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Record W3202239218 · doi:10.14198/medieval.19382

La pesca de esturión en el reino de Valencia, Aragón y Cataluña (siglos XIV-XV)

2021· article· es· W3202239218 on OpenAlexfundno aff
Pablo José Alcover Cateura

Bibliographic record

VenueAnales de la Universidad de Alicante Historia Medieval · 2021
Typearticle
Languagees
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsnot available
FundersUniversidad de Castilla-La ManchaUniversity of OxfordUniversity of LeicesterUniversity of ExeterUniversity of TorontoUniversidad de Alicante
KeywordsHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

Comer esturión era un claro indicador de rango social elevado. Su ingesta era utilizada para diferenciar las mesas de las clases altas de las no privilegiadas. Por ello, su pesca suscitó el interés de las élites, desde reyes a jurados y consejeros de ciudades y villas, quienes controlaron su captura. Los estudios de este pescado se han centrado en analizar su papel dentro de los sistemas alimentarios, su precio en las pescaderías y los restos óseos hallados en yacimientos arqueológicos. Sin embargo, no hay estudios que analicen su pesca, debido sobre todo a la escasez de fuentes escritas. El presente trabajo pretende realizar una primera aproximación a su captura dentro de la historiografía de la Corona de Aragón. El presente artículo selecciona las principales aportaciones en torno al esturión en el medievo y aporta nuevas fuentes cuya interpretación permite obtener las características generales de la pesca del selecto pescado. Además, se realiza una aportación desde la interdisciplinariedad, combinando resultados de la investigación en archivos y los más relevantes estudios de arqueofauna ibérica. Finalmente, se pretende llevar a cabo un estudio comparativo entre la captura de esturión en aguas fluviales de la Corona de Aragón con la del Reino de Inglaterra, de Hungría y Croacia y los Comuni para constatar elementos comunes.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.249
Teacher spread0.243 · 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.

Study designNot applicable
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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueAnales de la Universidad de Alicante Historia MedievalSame topicMaritime and Coastal ArchaeologyFrench-language works237,207