Correlation dimensions of climate subsystems and their geographic variability
Bibliographic record
Abstract
The correlation dimension D2 of precipitation (Canada and Africa), air temperature (Canada, New Zealand, and Southern Hemisphere), geo‐potential height (Canada), and unregulated streamflow (Canada, USA, and Africa) were estimated using the Hill procedure of Mikosch and Wang [1995] and the bias correction of Wang and Gan [1998]. After bias correction, it seems that D2 is distinct between climate subsystems, such that for precipitation, it is between 8 and 9, for streamflow, it is between 7 and 9, for temperature, it is between 10 and 11, and for geo‐potential heights, it is between 12 and 14. The results seem to suggest that climate might be viewed as a loosely coupled set of fairly high‐dimensional subsystems and that different climate variables can yield different D2 values. Further, results also suggest that the D2 values of the climate subsystems studied, generally, have low geographic variability, as found between the precipitation data of Western Canada and Uganda, between the streamflow data of basins representing wide range climate and scales from Canada, USA, and Africa, and among the temperature data of Western Canada, New Zealand, and the southern hemisphere, and that the original D2 values analyzed from Canadian geo‐potential heights are similar to that of Western Europe, eastern North America, and Germany. There is at most a weak relationship among basin physical characteristics, location, basin scale, and streamflow D2, while climatic influence is more obvious, as shown by drier basins having slightly higher D2 values than basins of wetter climate, basins from temperate climate having higher D2 values than those from cold or hot climates, and comparable D2 values between precipitation and streamflow data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".