Radiocarbon Wiggle Matching on Laminated Sediments Delivers High-Precision Chronologies
Bibliographic record
Abstract
ABSTRACT High-resolution sediment chronologies with the best possible time control are essential for comparing palaeoecological studies with independent high-precision climatic, archaeological or historic data in order to disentangle causes and effects of past environmental, ecological and societal change. We present two varved lake sediment sequences from Moossee and Burgäschisee (Swiss Plateau) that have chronologies developed with Bayesian models and radiocarbon (14C) dating of terrestrial plant macrofossils extracted from sediment samples with constant age ranges. We illustrate the potential of high-resolution14C dating for the construction of robust, high-precision sediment chronologies. The mean 2σ age uncertainties were reduced to±19 cal yr for Moossee and to±54 cal yr for Burgäschisee over the entire period of 3000 cal yr, while 2σ uncertainties of only±13 cal yr and±18 cal yr respectively, were achieved for shorter time intervals. These precisions are better than or comparable to those of previous varve studies. Our results imply that a sophisticated subsampling strategy and a careful selection of short-lived and well-defined terrestrial plant remains are crucial to avoid outlying14C ages. A direct linkage between palaeoeological studies with dendrochronologically dated, local archaeological sites as well as a precise comparison with high-resolution climate proxy data have become feasible.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".