Low impact development devices DNA of cities for long term stormwater management strategies
Bibliographic record
Abstract
Abstract In 2024 the Low Impact Development Devices (LID) open-source international database ClimateScan consist of over 14.000 climate adaption related projects uploaded in the period of 2014–2024. For cities with over 500 projects, this offers an opportunity to construct a LID-DNA of the city. LID-DNA presents the ‘genetic information of the development and functioning of LID in a city’ and was first used in The Netherlands during ClimateCafés as evaluation for future design and maintenance of stormwater management strategies. The LID-DNA of several cities based on the quantity and categories of LID is visualized. The LID structure of early adaptor Amsterdam with over 500 LID measures implemented in 2000–2024, shows a large variety of over 20 types of individual LID. The relative new adaptor Riga shows a LID-DNA with a focus on bio-filtration with raingardens and swales (based on 40 data points). Stakeholders from different departments concluded that cities benefit from the insights of their urban LID-DNA earlier in the process. An early insight will support a targeted LID strategy choosing a limited cost-efficient group of LID than having a wide range of different LID without evaluation of their efficiency. Departments in the city asked for more detailed insights (earlier in the process) to prevent mal-adaptation and disinvestments and be more efficient with their capacity. The ClimateScan database holds over 300 monitored LID projects with research results in North America and Europe in cities as Vancouver, New Orleans, Amsterdam and Riga. Future work will focus on more detailed LID-DNA visualisation based on not only the amount of LID but on the dimensions such as water storage (m 3 ) and surface (m 2 ). Monitoring of LID will be stimulated to make strategic decisions on measured infiltration rates (m/d) of LID as most important criteria for possible damage by floodings and maintenance (clogging). Raising awareness and capacity building targeted on the high-ranking cost-efficient LID is set up in both cities focused on the design, construction and maintenance of LID.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".