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Record W7069839687

Sobrevivir y amar

2022· other· es· W7069839687 on OpenAlexaboutno aff

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2022
Typeother
Languagees
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationStatistical analysisQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

Este es el trabajo final escrito por Andressa Derbli para la Maestría en Escrituras Creativas de
\nla Universidad Nacional de Colombia en la vertiente de Guion para Largometraje. Su
\nelaboración cumple las etapas teóricas de la cinematografía para un documento que da inicio
\na la realización de una película. Categorizado en el género de comedia dramática y con la
\nestructura clásica de los Tres Actos Aristotélicos, el guion de “Sobrevivir y amar” es una
\nficción que se enfoca en el carácter intimista y la creación de personajes profundos, amables
\ny divertidos. Sus subtramas, aunque a veces parecen que se desligan de la trama principal,
\ncontribuyen, desde distintos conflictos y acciones dramáticas, al arco de transformación del
\npersonaje principal. El trabajo contiene: título, tema, premisa, storyline, sinopsis, argumento,
\ndescripción de personaje, escaleta, nota de intención y guion dialogado. (Texto tomado de la fuente)

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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
GenreOther

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)Same topicMachine Learning in BioinformaticsFrench-language works237,207