Assessing Spatial Scale Effects on Hydrometric Network Design Using Entropy and Multi‐objective Methods
Bibliographic record
Abstract
Abstract In order to facilitate water resources decisions, it is important that accurate and informative hydrometric data are collected. Combining information theory with multi‐objective optimization has led to methods of optimizing the information content provided by hydrometric networks; however, there is no available study on the effects of spatial scale and data limitation on these methods. Herein, a dual entropy multi‐objective optimization (DEMO) and a transinformation (TI) analysis were done to recommend optimal locations for additional hydrometric stations in the Madawaska Watershed. This analysis was designed to be comparative to a similar study conducted on the Ottawa River Basin which encompasses the Madawaska Watershed to allow for an investigation of the spatial scale effects in this type of network design. This study concludes that TI analysis is not adversely affected by scaling; however, the DEMO analysis is sensitive to the placement of potential station locations and the size of the study area. This study also examines the benefit of including nearby stations when the area of interest does not have a sufficient number of existing hydrometric stations for analysis. It is shown that these stations can provide useful information because their inclusion in the analysis increased the average TI in the watershed. Recommendations were made as to the ideal locations of additional stations in the Madawaska Watershed hydrometric network.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".