Biogenic silica concentration as a high‐resolution, quantitative temperature proxy at Hallet Lake, south‐central Alaska
Bibliographic record
Abstract
High‐resolution, quantitative temperature records are valuable for placing recent warming in the context of long‐term, natural climate variability. Here we use biogenic silica (BSi) concentrations preserved in lacustrine sediment from an oligotrophic lake to quantitatively reconstruct air temperature at Hallet Lake in south‐central Alaska. Mean June through August temperature measured over the past 80 yr at Valdez (Alaska) correlate with BSi from Hallet Lake (r = 0.87, p = 0.01). We chose a nested function to model the non‐linear relation between summer temperature and BSi in the calibration data set, and to reconstruct temperature for the past 2 ka. Our BSi‐inferred temperature reconstruction shows synchronous changes with independent paleoclimatic proxies for southern Alaska, and provides evidence for a greater rate and magnitude of 20th century temperature warming at Hallet Lake than recorded by other quantitative temperature proxies in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.007 |
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; both teacher heads agree on what is shown here.
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".