Rensning, biodiversitet og rekreative værdier i våde regnvandsbassiner med flydeøer
Bibliographic record
Abstract
One of the most regular methods to delay and clean runoff rainwater is wet rainwater ponds. Wet rainwater ponds are recognized as BAT (Best Available Technology) in Denmark. The wet rainwater ponds are used nationally in Denmark.<br/><br/>Both function and requirements for the for separate rainwater outlets has changed a lot the past few years. The first rainwater ponds where dry and only filled with water when a need for delay volume of rainwater to avoid a negative hydraulic pressure at the recipient. In the last few years, the focus has changed to water quality and not only quantity. Rainwater from urban surfaces can have a negative impact on the recipients, and therefore must be cleaned properly. To clean the runoff rainwater, the rainwater pond needs a wet volume between 200 and 300 m3 pr reduced hectare. The wet volume will contribute to retention of substances from rainwater by sedimentation of particles. But it is also known that the retention of soluble substances is quite low. In this project preliminary tests have been started to see if it possible to increase the retention effect of wet rainwater ponds of selected nutrients and heavy metals connected to urban rainwater runoff.<br/><br/>This project took place in a controlled environment in an outdoor wet laboratory at the University of Southern Denmark. Four plastic tanks on 1 m3 were used. Each tank with its own inlets and outlets simulated four small wet rainwater ponds, in small scale. Through the project the tanks were exposed for rain events to reflect reality. The rain events were simulated with water from a real rainwater pond near University of Southern Denmark. All four tanks had a permanent wet volume and a different scenario. Tank 1 had a floating island with vegetation and coconut mat (which purpose is vegetation layer). Tank 2 had a floating island with vegetation and no coconut mat, whereas tank 3 had the floating island with coconut mat and no vegetation. The fourth tank was control tank, with nothing but water, to simulate a normal wet rainwater pond with no floating island.<br/><br/>Data is collected from both the water and the biomass in the same trials, which is unique for the research. The hypothesis was to see that the concentrations of the selected nutrients and heavy metals was decreased between the inlet and outlet. As well as an accumulation of the same soluble in the biomass. The project succeeded with a lot of new knowledge. But because of the very short trial period it wasn’t possible to achieve statistic representative data which can be interpreted as results. The experience and tendencies seen is this project will benefit our next project, which will continue the trials in real wet rainwater ponds in Odense.<br/><br/>Some of the biggest challenges in this project, were that the project was started in the end of the growing season for the vegetation as well as the very short trial period. The vegetation needed time to acclimatize to the new environment in the tanks in the end of their growing season. But at the same time the data collection needed to start. Another uncertainty on the tendencies of the data, was there no replicates. To convert the tendencies of the data to results, more projects with replicates must take place.<br/><br/>In this project we saw tendencies that could indicate floating islands might have a positive effect on the retention effect of wet rainwater ponds. The future work with documenting the effect will continue in the next years in a MUDP supported project with the same project group. At this moment it is not possible to deliver validated results on the effect of floating island in wet rainwater ponds from this project. But all experiences and data will be transferred to our next project with MUDP. <br/>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.754 | 0.360 |
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; both teacher heads agree on what is shown here.
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".