Multicriterion decision analysis approach to assess the utility of watershed modeling for management decisions
Bibliographic record
Abstract
This paper employs the multicriterion decision analysis (MCDA) technique in a novel way to address the efficiency of watershed instrumentation programs and the efficacy of watershed modeling. A case study of reconstructed watersheds in northern Alberta, Canada, is used to illustrate the proposed usage of the MCDA technique. The watersheds have been disturbed as a result of oil sands mining activities. Assessing the performance of the reconstructed watersheds with regard to restoring the hydrology of the disturbed watershed is a crucial issue for both the mining industry and other stakeholders. The problem is formulated in a multicriterion context. A payoff matrix containing seven evaluation criteria and three different soil covers as feasible alternatives is constructed. The system dynamics watershed (SDW) model is used to simulate the reconstructed watersheds over a period of 61 years using historical meteorological records. Accordingly, 61 payoff matrices that are populated using the results of the SDW model are evaluated. A multicriterion decision analysis framework is implemented to evaluate the different alternatives with respect to the chosen set of criteria. The three alternatives are ranked every year, and accordingly, the probability that a certain alternative dominates others is estimated. The alternative that has the highest probability of occupying the top rank over the period of analysis is indicated as the best alternative. The probability value is called the probability of making the right decision (PMRD). Various types of uncertainty analyses are conducted to evaluate the sensitivity of the final decision to changes in the scores of the evaluation matrices. An index, named the confidence in the PMRD, is developed to quantify the reliability of the results of the watershed model. The results highlight the utility of modeling as a possible alternative to some components of the intensive instrumentation program. Moreover, areas of deficiency and inaccuracies in the watershed model are identified for further improvements.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".