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Record W7112251892

Estudi i propostes de millora de la xarxa de bicicleta del municipi d'Eivissa

2022· dissertation· es· W7112251892 on OpenAlexaboutno aff

Bibliographic record

VenueUPCommons institutional repository (Universitat Politècnica de Catalunya) · 2022
Typedissertation
Languagees
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Nova scotiaCompetition (biology)
DOInot available

Abstract

fetched live from OpenAlex

El objetivo general de este trabajo es realizar una serie de propuestas de mejora y de la red de carriles bici teniendo en cuenta las peticiones de la ciudadanía en un marco técnico. En este trabajo, vamos a ver primeramente una breve diagnosis sobre la movilidad en bicicleta y VMP (vehículo de movilidad personal) a nivel nacional, y a nivel de la isla de Ibiza. Seguidamente consideraremos la importancia de actualizar e integrar los VMP a la red de carriles bici en la movilidad cotidiana junto a una clasificación de los tipos de usuarios de bicicletas y VMP por tipos de viajes, rango de edades etc. A continuación, veremos los objetivos del trabajo que están alineados con los objetivos que persigue el Plan de Movilidad Urbana Sostenible de Ibiza (PMUS) junto con una breve síntesis de este. Finalmente, teniendo en cuenta las infraestructuras existentes y aquellas que ya están en fase de proyecto, teniendo en cuenta las peticiones de la ciudadanía y propuestas del gobierno, empiezo un análisis tramo a tramo sobre toda la red de bici, justificando cada tramo corrigiendo y volviendo a trazar las rutas con un criterio holístico, técnico y basado en el sentido común.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.011
GPT teacher head0.296
Teacher spread0.285 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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