Bibliographic record
Abstract
This thesis seeks to investigate the changes in the characteristics of 20th and 21st century meteorological droughts in the UK to address the following lines of inquiry: 1) How credible are rainfall-based indices in representing hydrological droughts, 2) How coherent are droughts?, 3) Can alternative method of analysis provide new (or additional) information on the uncertainties in climate models?, and 4) Will future drought characteristics change?. Key results, respectively, are summarized as follows: • The drought severity index (DSI), can be considered a good proxy for assessing hydrological droughts as can be seen in its ability to capture the major hydrological events. The main caveat of the DSI is that there is often an underestimation of drought intensity and duration, • Drought covariance is higher for the (i) wet season, (ii) moderate and (iii) shorter duration droughts, • Evaluating climate models using drought statistics produces contrasting results compared with that using the model's precipitation fields. Drought statistics show biases which are largely negative, more intense, and have a greater spatial coverage. • The projected ensemble-mean change is generally greater (and more widespread) for (i) moderate droughts and (ii) wet season compared with extreme droughts and the dry season, respectively with increases in drought intensity, drought covariance index, frequency of drought months and frequency of drought events for a given duration shown for England.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".