On-Line Submission: Real Time Control of Sewers: Overview and State of the Art
Bibliographic record
Abstract
time control (RTC) is increasingly gaining acceptance in the mainstream practice of managing wastewater conveyance networks. However, although the concepts, and even some applications, have been around for a number of years, is still not common in wastewater conveyance networks. Introduction and Definition One of the barriers to broader acceptance of may be an unfortunate perception that systems are always complex. In a generally risk-averse culture of public agencies, there is often reluctance to adopt the bleeding edge methodologies and tools. In recent years, the term Real Time Control of Sewer Systems has often been used to describe control systems that include system-wide (global) control rules, and may include such sophisticated components as linear optimization algorithms (e.g. Seattle, Hamilton, Quebec City). As some of these complex systems have been reported in the literature, for many in the wastewater industry the term RTC has somehow become synonymous with this type of complex system and application. Therefore, when municipalities consider RTC, they might start from this narrow and specific interpretation of the term as implemented in a global predictive optimal configuration. Such a complex system is by no means always the best choice, and therefore a broader and more appropriate definition of an system may be: An system performs control action in real time, adjusting the operation of facilities in response to observed or measured conditions. Historically, flows and levels in sewer systems used to be only manipulated by static facilities, e.g. weirs, that were not being adjusted in real time. adds the dynamic component, where some of the facilities are actively adjusted in real time based on system conditions.
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 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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.035 |
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".