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Record W3112348650 · doi:10.20318/recs.2020/5555

Vigilancia de la publicidad de productos químicos para piscinas comercializados por internet

2020· article· es· W3112348650 on OpenAlexaff
Joaquín Gámez de la Hoz, Ana Padilla Fortes, Marta Padilla-Ruiz

Bibliographic record

VenueREVISTA ESPAÑOLA DE COMUNICACIÓN EN SALUD · 2020
Typearticle
Languagees
FieldPsychology
TopicSafety Warnings and Signage
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Introducción: La comunicación de los peligros asociados a los productos químicos es un aspecto fundamental para mejorar la confianza de los consumidores en el comercio electrónico. Objetivo: Examinar las indicaciones de peligro reglamentarias asociadas con productos químicos para piscinas puestos a disposición de terceros a través de su publicidad por internet. Metodología: Se seleccionaron aleatoriamente 40 productos químicos peligrosos de piscinas comercializados en tiendas online a través de internet procedentes de 8 empresas con sede social en Andalucía. Resultados: Predominaron los productos sin indicaciones de peligro visibles junto a su publicidad (n=25). Las indicaciones de peligros en las etiquetas de los productos en la web resultaron ilegibles. Únicamente 3 productos mostraron los códigos y frases de peligro visibles en el sitio web. Conclusión: La protección de consumidores y usuarios de comercio electrónico en materia de seguridad química puede verse comprometida debido a la falta de información relevante sobre la peligrosidad de los productos comercializados a través de internet.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.028
GPT teacher head0.340
Teacher spread0.311 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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