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Record W7113052901

Contaminación ambiental: identificación y cuantificación de microplásticos en aguas costeras del litoral andaluz occidental.

2024· other· es· W7113052901 on OpenAlexaboutno aff

Bibliographic record

VenueRODIN (Universidad de Cádiz) · 2024
Typeother
Languagees
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCoastal zoneAquatic environmentNova scotia
DOInot available

Abstract

fetched live from OpenAlex

La contaminación por microplásticos se ha convertido en una de las principales preocupaciones medioambientales de la sociedad actual. Variedad de estudios han documentado la presencia y distribución de microplásticos de diferentes tamaños, formas y composiciones poliméricas en agua marina y fluvial, sedimentos y organismos alrededor del mundo. Este Trabajo Fin de Grado proporciona un análisis de la distribución, abundancia y diversidad de los microplásticos en aguas costeras del litoral occidental andaluz (Golfo de Cádiz). Se seleccionaron 10 puntos de muestreo en Huelva, el estuario del río Guadalquivir y la Bahía de Cádiz, basados en su relevancia ecológica (por su proximidad a áreas protegidas) y en la presencia de presión antropogénica (agricultura, acuicultura, plantas de tratamiento de aguas residuales, etc.). Los resultados obtenidos muestran que las formas más abundantes en todas las muestras fueron los fragmentos, seguidos por las fibras, y finalmente las esferas y filamentos fueron las formas menos abundantes. El rango de tamaño más abundante en los microplásticos fue el superior a 63 µm, y la concentración total de microplásticos varió desde 37,67 ± 27,08 MPs/L (Estuario del Guadalquivir) hasta 124,36 ± 7,86 MPs/L (Bahía de Cádiz). Se identificaron un total de 14 polímeros, los más frecuentes fueron: PP, PTFE, CPE, PS, PE, PVC, PA y PL. El PP y el PTFE fueron los polímeros más abundantes y se identificaron en todas las muestras.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.003
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0040.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0140.175

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 head0.011
GPT teacher head0.287
Teacher spread0.275 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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