Transforming The Relationships Between Geoscientists and Urban Decision-Makers: European Cost Sub-Urban Action (TU1206)
Bibliographic record
Abstract
The European COST Sub-Urban Action (TU1206) has had the fundamental aim of closing the knowledge gap between subsurface experts and potential users of subsurface knowledge - urban decision- makers, practitioners and researchers. The Action assembled a network involving >30 countries, 23 actively participating cities, researchers, practitioners and urban decision-makers, and brought together the fragmented research and good practice across Europe in sustainable urban sub-surface use. Development of national exemplars has been encouraged, and good practice identified to inspire others, using a lighthouse-follower approach to cascade knowledge and good practice across Europe and further afield. Experts from both sides of the knowledge gap were brought together to assess and synthesise the state-of-the-art in lighthouse cities with respect to urban sub-surface knowledge, understanding, and use of that knowledge. This was achieved in 19 City Studies, with findings encapsulated in an over-view report “Out of Sight - Out of Mind”. Expert sub-groups then identified good practice in subsurface data and knowledge locally, nationally, and Europe-wide. These are highlighted in the synthesis report “Opening up the subsurface for the cities of tomorrow”, and expanded on in seven topic review reports. These also identified key gaps in knowledge, and its use. A new concept, GEOCIM is proposed for City Quarter to Conurbation scales, combining subsurface and above-ground models. These enable: a.) holistic urban planning; b.) identifying subsurface opportunities; and c.) saving costs by reducing uncertainty in ground conditions. Finally, the Action’s reports and outputs were integrated within an online toolbox, and will be further outlined in the Final Report of the Action, to be made available on the Action’s website. The Sub-Urban Toolbox promotes and disseminates the good practice, and decision-support tools: a.) to help better inform and empower city decision- and policy-makers about the sub-surface and the vital importance of its early-stage consideration; and b.) accelerate uptake amongst sub-surface experts of sub-surface modelling workflows. Users with different backgrounds and needs, require different access to, and appropriate translations of, the Sub-Urban Toolbox. Therefore different entry points are provided for sub-surface technical experts, and urban planners, and decision- and policy-makers. As the critical mass of city decision- and policy-makers that is better aware of the sub-surface and its sustainable use expands, the potential for higher level policy consideration of the subsurface grows, and a wider range of impacts will become achievable.
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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 | 0.070 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".