Quantification of the Diminishing Earth’s Magnetic Dipole Intensity and Geomagnetic Activity as the Causal Source for Global Warming within the Oceans and Atmosphere
Bibliographic record
Abstract
Quantitative analyses of actual measurements rather than modeling have shown that “global warming” has been heterogeneous over the surface of the planet and temporally non-linear. Residual regression analyses by Soares (2010) indicated increments of increased temperature precede increments of CO2 increase. The remarkably strong negative correlation (r = -0.99) between the earth’s magnetic dipole moment values and global CO2-temperature indicators over the last ~30 years is sufficient to be considered causal if contributing energies were within the same order of magnitude. Quantitative convergence between the energies lost by the diminishing averaged geomagnetic field strength and energies gained within the ocean-atmosphere interface satisfy the measured values for increased global temperature and CO2 release from sea water. The pivotal variable is the optimal temporal unit employed to estimate the total energies available for physical-chemical reactions. The positive drift in averaged amplitude of geomagnetic activity over the last 100 years augmented this process. Contributions from annual CO2 from volcanism and shifts in averaged geomagnetic activity, lagged years before the measured global temperature-CO2 values, are moderating variables for smaller amplitude perturbations. These results indicated that the increase in CO2 and global temperatures are primarily caused by major geophysical factors, particularly the diminishing total geomagnetic field strength and increased geomagnetic activity, but not by human activities. Strategies for adapting to climate change because of these powerful variables may differ from those that assume exclusive anthropomorphic causes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".