Coles_etal_global_resource_review_supp_mat_2025_10_08.pdf from A review of global tidal stream energy resources
Bibliographic record
Abstract
This review identifies 426 candidate sites with potentially suitable characteristics for tidal stream energy development, across 19 countries in Europe, the Americas, Asia and Australasia. The most common site assessment quantifies the theoretical resource, which is the maximum amount of total energy that can be extracted. The aggregated theoretical resource estimate, of 1000, TWh/year, from 262 sites (62% of those identified), across 6 countries, is equivalent to 115 GW of continuous annual power. A more informative, albeit less common assessment, considers technical, environmental and economic constraints on energy extraction. New data from UK assessments is presented that indicates relationships between the theoretical and this more practical level of energy extraction, which are used to derive practical levels of electricity generation at other sites across the world. Results indicate a quasi-practical resource of 110 TWh/year from 90 sites (20% of the candidate sites) across the UK, France, Canada, USA, China and New Zealand. When assessed against national/regional electricity production, the UK, Indonesia and New Zealand show the greatest potential to make national-scale electricity supply contributions, whilst France, Canada, USA and China exhibit lower, regional-scale impact potential. Resource estimation is highly sensitive to turbine/array design and constraints, and studies adopt a wide range. Consequently, reported P10 and P90 resource estimates can lie 40% above/below their P50 estimate, respectively. Recommendations are made to characterize this sensitivity of the resource to these drivers.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.237 | 0.097 |
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".