Out of the woods: Driftwood insights into <scp>H</scp>olocene pan‐<scp>A</scp>rctic sea ice dynamics
Bibliographic record
Abstract
Abstract The collation of 913 driftwood samples from across the western Arctic, with spatiotemporal distribution and available provenance data, enabled the production of a high‐resolution proxy‐based reconstruction of Holocene Arctic Ocean surface current and sea ice dynamics. Regionally bounded, driftwood‐based sea ice reconstructions studies suggest spatiotemporally complex past Arctic sea ice extent and movement; however, a large‐scale compilation of Holocene Arctic driftwood has not previously been developed. Sparse driftwood in the early Holocene (≥8.2 cal ka B.P.) deglacial period was followed by increased driftwood deposition in the warmer mid‐Holocene (8.2–4.2 cal ka B.P.); characterized by an enhanced Transpolar Drift (TPD) ∼7 cal ka B.P., leading to sea ice loss through the Fram Strait. Driftwood incursion peaks show spatial E‐W progression from the Eurasian Archipelagos to Greenland and the Canadian Arctic Archipelago, suggesting a progressive shift in the orientation of the TPD on centennial‐millennial time scales and intermediate phases in the Arctic Oscillation. Late Holocene cooling (≤4.2 cal ka B.P.) is indicated by increased influx of probably North American Picea via a strengthened Beaufort Gyre (BG) which enhanced sea ice recirculation, starting in the western Arctic and progressing eastward. In recent millennia (<2 cal ka B.P.), a more variable driftwood record alternates between BG and TPD dominance on centennial time scales. To further constrain a spatiotemporal reconstruction of variations in Holocene ocean current and sea ice dynamics, a more definitive determination of driftwood provenance is recommended to build upon the current framework, such as through radiogenic isotope tracing and aDNA analysis.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".