Bibliographic record
Abstract
This paper presents the study case of the Gare Maritime, a former train station in Brussels in Belgium that covers an area of 40.000m² and that has been entirely renovated to welcome under its roof the construction of twelve multilevel pavilions in fully timber structure.Those 4500 m² of twelve new buildings are hosting shops, publics functions and offices and creates a new covered quarter inside the city.This paper introduces first the project, its historical context, its situation and its refurbishment.Then an explanation of the structural design is given and divided in different parts.A focus is first made over the specific features of the project to explain the challenges of the structure.Then the joints developed on the project are explained explaining the main type of assemblies.Thirdly, all the fire engineering process of the project is described; how the dossier was accepted by the authorities, the simulations and calculations that needed to be done.The last section finally gives information about the mounting process and the upstream preparation that it required.In the next chapter, more can be learned over the innovative technologies that have been implemented on the project to make it completely energy neutral and fossil free before finally concluding and giving some important numbers over the project.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".