Century-scale variability in late-summer rainfall events recorded over seven centuries in subannually laminated lacustrine sediments, White Pass, British Columbia
Bibliographic record
Abstract
Abstract Formation of annually laminated sediments in Summit Lake, White Pass, British Columbia is controlled by runoff generated by snowpack and glacier melt and major rainfall events. The 700-yr varve record is divided into two subannual series (early and late) based on sedimentological criteria and sedimentary structures within each varve. A comparison of recent subannual laminae with nearby meteorological records supports the interpretation they are formed by river discharge events generated by major snow and glacier melt events and large late-summer rainfall events. A significant correlation exists between the late subannual thickness series and the size of the largest rainfall events in late summer. The long record indicates there was an abrupt increase in the thickness and frequency of major rainfall-induced sedimentary events at the end of the seventeenth century. In addition, the frequency of laminae generated by early runoff events also increased. However, early subannual varve thickness component remains statistically the same as the thickness prior to the end of the seventeenth century. This suggests the change in varve thickness at this time is due to increases in major late-summer rainfall frequency rather than increased sediment availability caused by regional Little Ice Age glacier advances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".