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Record W2548104366 · doi:10.1016/j.rmb.2016.09.005

Identificación del ostión americano Crassostrea virginica (Mollusca: Bivalvia: Ostreidae) como recurso natural en las Antillas Mayores: Cuba

2016· article· es· W2548104366 on OpenAlexaff
Abel Betanzos-Vega, César Lodeiros, José Espinosa-Sáez, José Manuel Mazón‐Suástegui

Bibliographic record

VenueRevista Mexicana de Biodiversidad · 2016
Typearticle
Languagees
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsCanadian Bank Note Company (Canada)
Fundersnot available
KeywordsBiologyHumanitiesGeographyZoologyPhilosophy

Abstract

fetched live from OpenAlex

El ostión de Mangle Crassostrea rhizophorae (Guilding, 1828) ha sido considerado la única especie de ostión nativo que se comercializa en Cuba. Sin embargo, se han encontrando agregaciones de «ostión de fondo» en las cuencas de los ríos Cuyaguateje y Cauto de Cuba, con morfología y hábitat diferentes a C. rhizophorae, cuya extracción contribuye a la producción ostrícola nacional. El presente estudio tuvo como objetivo la caracterización fenotípica de ambos ostiones en 3 zonas de estas cuencas, mostrando evidencias de dicotomías fenotípicas y de hábitat entre ambos recursos pesqueros, mostrando a Crassostrea virginica (Gmelin, 1791) como la especie denominada «ostión de fondo». Esta mostró una talla (promedio 61.1 ± 23.46 mm, máxima 145 mm) superior al 65% de la del ostión de mangle C. rhizophorae (promedio 37.2 ± 11.95 mm, máxima 87 mm), así como un mayor rendimiento (4.5-10.2% de carne con respecto al peso total) que las poblaciones del ostión de mangle (3.9 a 7.1%). Este estudio registra por primera vez la especie C. virginica como recurso natural en explotación comercial en Cuba y su mayor rendimiento evidencia un potencial como especie cultivable en las Antillas Mayores.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.008
GPT teacher head0.247
Teacher spread0.238 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

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