Greenland Temperatures and Solar Activity: A Computational Intelligence Approach
Bibliographic record
Abstract
The complexity of the Earth's climate and its relationship with solar activity are here approached by means of two computational intelligence techniques: multivariate time series model mining (MVTSMM) and genetic programming (GP). They were applied to a temperature record (Delta 018/16), obtained from an ice core in central Greenland, representative of the climate variations in the North Atlantic regions, and the International Sunspot Number series, as a proxy of solar activity, both covering the period from 1721 to 1983. Several experiments were conducted using these records jointly and separately with the purpose of characterize and reveal their time dependencies. Preliminary results show this mining approach is a valid and promising research line. The time-lag spectra obtained with MVTSMM seem to point out to time stamps of some of the most important Earth-climate and solar variations, as well as the contribution of solar activity and sunspot solar cycles along time. The GP provided equations which approximate the relative contribution of particular solar time-lags. Although suggestive, this research is at an early stage and the results are preliminary, emphasizing methodological aspects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".