Multicriteria Decision Analysis for Wave Power Technology in Canada
Why this work is in the frame
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Bibliographic record
Abstract
Three individual wave power generation technologies were studied and evaluated using multicriteria decision analysis through the use of the PROMETHEE method. To evaluate the three technologies, data were collected from previously performed experimental testing on the performance of each wave power generation technology. These data were used to feed into seven different criteria; namely the capacity factor, rated power, capital cost, operation and maintenance (O&M) costs, cost of electricity (COE) for a 10 year payback, maturity, and survivability. The associated data and criteria were used to determine the optimal technology. The results from the Decision Lab modeling ranked the Wave Dragon, AquaBuOY, and Pelamis technologies as 1, 2, and 3, respectively, for all three locations: Tofino/Ucluelet, Hibernia Oil Platform, and St. John's, Newfoundland. A sensitivity analysis of the threshold values determined for the baseline modeling indicated that the original ranking was essentially unaffected when the threshold values were modified (increased and decreased). The weights of the criterion were individually adjusted to evaluate any change in ranking order. A sizable increase in weighting of greater than 40% of any one criterion (while the others were weighed equally) resulted in a change of the overall ranking order of the three technologies. Final weightings on each of the criterion were assigned with preference on rated power, COE, and maturity stage. All other criteria were weighted equally and like the baseline modeling output, the results of the model ranked Wave Dragon, AquaBuOY, and Pelamis from most favorable to least favorable for all three of the locations analyzed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it