MétaCan
Menu
Back to cohort
Record W2594843494 · doi:10.46430/phes0024

Reutilización de código y modularidad en Python

2017· article· es· W2594843494 on OpenAlexaff
William J. Turkel, Adam Crymble

Bibliographic record

VenueThe Programming Historian en español · 2017
Typearticle
Languagees
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsHumanitiesPhysicsPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Los programas de computadora pueden resultar largos, inmanejables y confusos si no contamos con mecanismos especiales para la gestión de su complejidad. Esta lección te mostrará la manera de reutilizar partes de su código mediante la escritura de Funciones y cómo fraccionar tus programas en Módulos con el fin de mantener todo de una manera concisa y fácil de depurar.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.008
Open science0.0030.007
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0110.004

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 head0.019
GPT teacher head0.298
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe Programming Historian en españolSame topicComputational Physics and Python ApplicationsFrench-language works237,207