Long-term river discharge and multidecadal climate variability inferred from varved sediments, southwest Alaska
Bibliographic record
Abstract
Abstract Sedimentological analyses of 289 years (AD 1718–2006) of varved sediment from Shadow Bay, southwest Alaska, were used to investigate hydroclimate variability during and prior to the instrumental period. Varve thicknesses relate most strongly to total annual discharge (r2 = 0.75, n = 43, p < 0.0001). Maximum annual grain size depends most strongly on maximum spring daily discharge (r2 = 0.63, n = 43, p < 0.0001) and maximum annual daily discharge (r2 = 0.61, n = 43, p < 0.0001), while varve thickness is poorly correlated with maximum annual grain size (r2 = 0.004, n = 287, p = 0.33). Relations between varve thickness and annual climate variables (temperature, precipitation, North Pacific (NP) and Pacific Decadal Oscillation (PDO) indices) are insignificant. On multidecadal timescales, however, regime shifts in varve thickness and total annual discharge coincide with shifts in NP and PDO indices. Periods with increased varve thickness and total annual discharge were associated with warm PDO phases and a strengthened Aleutian Low. The varve-inferred record of PDO suggests that any periodicity in the PDO varied over time, and that the early 19th century marked a transition to a more frequent or detectable shifts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".