Bibliographic record
Abstract
In this paper, a hybrid model, GA–GHCA, composed of the genetic algorithm (GA) and the general hybrid cellular automata (GHCA) is proposed for the efficient and effective optimal design of pumped sewer networks with fixed layout. The GHCA model was recently introduced by the authors with considerable success for the optimal design of sewer networks. Two alternative versions of the GA–GHCA model are proposed. In the first approach, the pump locations and the corresponding pumping heads are decided by the GA model, while the diameter and nodal cover depths of the network pipes are optimally determined by the GHCA model considering the predefined pump locations and their pumping heights defined by the GA. In the second model, however, only the pump locations are decided by the GA model and for each GA individual, the network characteristics including the pipe diameters, pipe nodal cover depths, and the pumping heights at the predefined locations are determined by the GHCA model. The proposed GA–GHCA model is tested against a benchmark example of pumped sewer network and the results are presented and compared to those of the existing methods. The results indicate that the proposed method is more efficient and effective than alternative methods for the optimal design of pumped sewer networks.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".