Bibliographic record
Abstract
In the southwestern United States, Lakes Powell and Lake Mead are taking the responsibility of supplying water and generating electricity to the surrounding five states of Arizona (AZ), California (CA), Wyoming (WY), New Mexico (NM), and Colorado (CO). Accordingly, we have established three models, and have established them and conducted sensitivity analysis combined with changes of various conditions in reality. In combination with multiple water supply needs, Model 1 optimizes the economic benefits from water and power supply of the five states as objective functions. In setting the constraints, the model takes into account such factors as water balance, minimum demand for water and power supplies, lake level requirements, and issues of sovereignty in downstream Mexico. Considering the influence of seasons on various factors, the decision variables are the water supply flow and power supply flow of each state on a quarterly basis. When solving the model, IA-PSO is used for optimization, reducing the possibility of local optimal solution.The optimization result is the optimal distribution of the power and water supply flows provided by the two lakes to the states over the four quarters of a year. In the case of CO,when the supply exceeds the demand, the time to provide the optimized water supply flow to meet its one-quarter water consumption demand is calculated to be 57.5 days; while when the supply fails to meet the demand, the additional water supplement required is 0.8×109m3.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".