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Record W2909158666 · doi:10.35537/10915/71256

Generación fotoquímica y caracterización de aductos pterina-pirimidina en nucleósidos, oligonucleótidos y ADN

2018· dissertation· es· W2909158666 on OpenAlexaff
Sandra Estébanez Ruiz

Bibliographic record

Venuenot available
Typedissertation
Languagees
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsFirst Nations University of Canada
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

El objetivo general de esta tesis doctoral es investigar los procesos fotosensibilizados por pterinas de bases pirimidínicas que forman parte de diferentes sustratos en condiciones aneróbicas. Para ello se trabajó con Ptr, que es el compuesto modelo no sustituido de las pterinas oxidadas. Como sustratos se emplearon nucleótidos, nucleósidos, oligonucleótidos y ADN eucariota. A continuación se detallan los objetivos específicos que han dirigido el desarrollo de la tesis: - Estudiar el proceso fotosensibilizado del nucleósido de timina por Ptr bajo diferentes condiciones experimentales y dilucidar el mecanismo de reacción. - Comparar la eficiencia de los procesos fotosensibilizados en nucleótidos y nucleósidos por Ptr en condiciones anaeróbicas. Concretamente, realizar un estudio sobre la generación de aductos fluorescentes entre el fotosensibilizador y el sustrato. - Caracterizar estructuralmente dichos aductos. - Analizar las propiedades fotofísicas de aductos pterina-timina. - Investigar la generación de aductos fluorescentes en oligonucleótidos, tanto de hebra simple como de hebra doble. - Estudiar el daño sufrido por moléculas de ADN eucariota en procesos fotosensibilizados por Ptr. - Realizar un análisis acerca de la formación de aductos pterina-timina en moléculas de ADN.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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 designBench or experimental
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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