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Record W2416075755

Case studies: results and synthesis projet 7FP CLOSER (Connecting LOng and Short-distance networks for Efficient Transport) Rapport de recherche Deliverable 5.2 project européen CLOSER.

2012· preprint· en· W2416075755 on OpenAlexaff
Petter Christiansen, Olav Eidhammer, Jardar Andersen, Alain L’Hostis, Giannis Adamos, Lucas C. Parra, E Ruiz-Ayucar, Tuuli Järvi, Corinne Blanquart

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2012
Typepreprint
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsMinistère des Transports
Fundersnot available
KeywordsDeliverableComputer scienceEngineeringSystems engineering
DOInot available

Abstract

fetched live from OpenAlex

The CLOSER project has been set to analyse the interfaces and interconnectionsbetween long distance transport networks and local/regional transport networks of allmodes. The project is funded within the Seventh Framework Programme of theEuropean Commission, under the topic TPT-2008.0.0.13 “New mobility/organisationalschemes: interconnection between short and long-distance transport networks”.The objective of WP5 of CLOSER is to accomplish in-depth case studies to deepenand validate the understanding of results obtained in Work packages 2, 3 and 4. Thiswill be achieved by:- Developing a joint assessment and evaluation framework for the case studies,incorporating knowledge that has been obtained in WP 2, WP 3 and WP 4- Carrying out the case studies- Synthesising the results of the case studies in order to give inputs for thedevelopment of recommendations in WP 6.The deliverable at hand summarises the seven case studies that have been conductedin the CLOSER project: Leipzig-Halle airport (Germany) Armentiéres station (France) Oslo bus terminal Vaterland (Norway) Port of Helsinki (Finland) Thessaloniki port (Greece) Constantza port (Romania) Vilnius Airport (Lithuania)

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)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.093
GPT teacher head0.293
Teacher spread0.201 · 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 designSimulation or modeling
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

Citations7
Published2012
Admission routes1
Has abstractyes

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