Ruim baan voor duurzamer beton: resource based engineering maakt duurzamere betonconstructies mogelijk die veilig en betaalbaar zijn
Bibliographic record
Abstract
In deze paper laten we zien hoe dat kan als je informatie over lokaal beschikbare, meer milieuvriendelijke grondstoffen centraal zet in het ontwerpproces: resource based engineering. Het doel van dit paper is om te laten zien dat er een nieuwe aanpak voorhanden is voor een duurzamere betonsector, een samenspel tussen data, materiaalmodellen en optimalisatie-software. We noemen dat samenspel Materiaalgedreven Multi-criteria Ontwerpoptimalisatie, MIMO. De MIMO-aanpak kan initiatieven gaan ondersteunen die nu al door de sector worden ontwikkeld, zoals hergebruik van beton, nieuwe betonsoorten met minder cement, slanker construeren en demontabel bouwen. De basis is gelegd en van hieruit kunnen we met de sector samen aan de slag om de transitie te maken naar grootschalige resource based engineering. Zo geven we ruim baan aan duurzamer beton.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".