Bibliographic record
Abstract
Numerous computer models for river basin planning and management have been developed and used extensively since the mid‐1970s. Early developments have relied on the use of network flow algorithms (NFA), due mainly to higher execution speed than the standard Simplex solvers. However, subsequent efforts to include proper modeling of hydraulic and hydrologic constraints introduced iterative schemes into the NFA‐based models, which diminished the initial advantages in execution speed and which also caused concerns over the accuracy of the convergence schemes. Hence full‐blown commercial linear programming (LP) solvers were introduced as a replacement to the iterative solution strategy of the NFA approach. This paper demonstrates one possible failure to solve a simple allocation problem using the NFA‐based model and shows how this problem can be solved using the standard LP approach. It then identifies cases when even a full‐blown LP approach cannot properly model two critical aspects of river basin management, one related to reservoirs with multiple outflows and the other one related to modeling of hydrologic channel routing. For NFA‐based models the failures are the result of the inability to include relationships between flows on different model components directly into the search process. For the models based on LP solvers, the failures are caused by the fact that integrated reservoir outflow capacity between the starting and the ending storage levels is assumed over the entire length of the assumed time step, while the actual outflow can only take place during the portion of the time step when the storage level is above the invert of the outlet structure.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".