KAVA Kartläggning av vattenanvändning
Bibliographic record
Abstract
KAVA mapping of water use. There is limited data on how much drinking water is used for different purposes and how water use varies over time in relation to various factors such as temperature and precipitation. This lack of knowledge means that we don’t know where the water goes for example on hot days when usage increases significantly, which creates a load on raw water sources, drinking water treatment and/or drinking water distribution systems. This project has analyzed water consumption data from a total of seven participating water utilities to create an understanding of how consumers contribute to flow peaks. Data has been collected from water treatment plants, pumping stations and water meters from various consumer groups such as households and businesses together with weather data. The results show, among other things, that water use, in most cases, rises when the temperature (maximum daily temperature) rises, that villas with a pool have a higher water use than villas without a pool during the second quarter of the year. Results also shows that and that flow peaks occur when many people use a little more water than when a few people use a lot more water, and that flow peaks are driven by local conditions as they usually do not occur simultaneously for drinking water plants in different locations. As more water meters with stationary readings are replaced with digital water meters, new opportunities are created to analyze water usage data. The new data base also provides opportunities to inform and visualize water use for consumers and give them direct feedback when they change their behavior pattern. The most important experience that is highlighted regarding communication of sustainable water use is to stick to a predetermined communication plan and to convey a clear and well-thought-out why consumers should reduce their water use. We hope that this project will create a better understanding of how and when flow peaks occur and with that information water utilities can better avoid flow peaks, irrigation bans and events with depressurized distribution networks from occurring.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".