d4PDF-WaveHs: the first SMILE-based ensemble of global historical wave height
Bibliographic record
Abstract
The d4PDF-WaveHs dataset represents the first single model initial-condition large ensemble (SMILE, 100-member) of historical significant ocean wave height (Hs) at a global scale. It was produced using an advanced statistical model with predictors derived from Japan's database for policy decision-making for future climate change (d4PDF) ensemble of historical simulations of sea level pressure. d4PDF-WaveHs provides 100 realizations of Hs for the period 1951-2010 (hence 6,000 years of data) on a 1° x 1° latitude-longitude grid. In addition, this dataset contains 14 statistics (including extreme indices) calculated on monthly, seasonal, and annual scales. d4PDF-WaveHs provides unique data to understand better the poorly known role of internal climate variability in ocean wave climate. For example, it can better distinguish climate variability from trend signals. It also provides a better sampling of the entire probability distribution, including the tails where extreme events occur. This is crucial to properly assess wave-driven impacts, such as extreme sea levels on low-lying (and densely) populated coastal areas. This dataset may interest a variety of researchers, engineers, and stakeholders, including those in the fields of climate science, oceanography, coastal management, offshore engineering, and energy resource development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.025 |
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".