Solar activity expressed in a modern varve thickness sequence
Bibliographic record
Abstract
Annually laminated sediments (varves) form in particular depositional settings, e.g., where seasonal climate produces fluctuations in runoff volume; variations in runoff affect the amount and type of sediment delivered to a catchment. Prior studies confirm that variations in selected varve traits correlate with inter-annual climate signals. In some locations, solar activity also appears to be expressed in varve characteristics, either through a direct effect or indirectly via influence of solar activity on climate. Evidence from proglacial Iceberg Lake, Alaska, indicates that solar activity may have directly contributed to varve deposition. A varve thickness sequence is compared to sunspot observations from 1610–1995 CE. Maunder and Dalton minima are clearly expressed in a varve power spectrogram; varve signal amplification beginning ca. 1950s CE coincides with increasing activity evident in a sunspot spectrogram, features that are only vaguely discernible in the raw time-series plots. Spectral relationships at sunspot periodicities are consistent with direct solar forcing of varve thickness, independent of any effect solar activity might otherwise have on climate. Simulations based on a meltwater model indicate that direct forcing could result from amplified ultraviolet (UV) emission during solar maxima, combined with lower UV albedo of glacial ice. The plausible forcing mechanism bolsters epistemology for concluding a cause–effect relationship: solar variability likely contributed directly to inter-decadal patterns in Iceberg Lake varve thicknesses. The putative effect could be enhanced at higher latitudes, where Earth’s atmosphere absorbs less of the UV energy emitted by the Sun; periods of lowered ozone concentration near the poles would exacerbate the natural abetting UV phenomena, potentially linking human activity to recent and accelerated polar ice cap melting.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".