Bibliographic record
Abstract
Weather observations are routinely used to analyse the past climate. Climate reanalyses may also be used for this purpose. These give a numerical description of the recent climate and are produced by combining models with weather observations. They contain estimates of atmospheric parameters such as air temperature, pressure and wind at different heights above the ground, and surface parameters such as precipitation, soil moisture content and temperatures, and sea-surface temperature. Because they are carried out using a fixed version of a forecast model and a data assimilation system which utilises historical observations, they produce parameters \nthat are physically consistent and often not routinely observed. Thus, climate reanalyses have the potential to extend the knowledge gained from current observation networks. Over the past few years researchers at Met ?ireann have produced a climate reanalysis dataset, called M?RA - Met ?ireann Reanalysis, for the period 1981-2017 for an area covering Ireland, the UK and northern France. \nThis dataset was launched in May 2017 and currently has over 100 users in Ireland, the U.K., Germany, the \nNetherlands, Canada and the U.S. On May 17th 2018 we held the first workshop for users of the data. The \nworkshop consisted of 15 very interesting talks spread across sessions on climate applications, precipitation \nand hydrology, energy, waves and storms. Short papers on a number of these talks are included in this workshop proceedings.
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How this classification was reachedexpand
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.061 |
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; both teacher heads agree on what is shown here.
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".