A fistful of shells: amplifying sclerochronological and palaeoclimate signals from molluscan death assemblages
Bibliographic record
Abstract
Abstract A growing body of literature is using sclerochronological information to infer past climates. Sclerochronologies are based on series of skeletal growth records of molluscs that have been correctly aligned in time. Incremental series are obtained from a number of shells to assess the temporal control and improve the climate signal in the final chronology. Much of the sclerochronological theory has been adopted from tree-ring science, due to the longer tradition and more firmly established concepts of chronology construction in dendrochronology. Compared to tree-ring studies, however, sclerochronological datasets are often characterized by relatively small sample size. Here we evaluate how effectively palaeoclimatic signal can be extracted from such a suite of samples. In so doing, the influences of the very basic methods that are applied in nearly every sclerochronological study to remove the non-climatic growth variability prior to palaeoclimatic interpretations, are ranked by their capability to amplify the desired signal. The study is performed in the context of six shells that constitute a bicentennial growth record from annual shell increments of freshwater pearl mussel. It was shown that when the individual series were detrended using the models set by the mean or the median summary curves for ageing (that is, applying Regional Curve Standardization, RCS), instead of fitting the ageing mode statistically to each series, the resulting sclerochronology displayed more low-frequency variability. Consistently, the added low-frequency variability evoked higher proxy–climate correlations. These results show the particular benefit of using the RCS method to develop sclerochronologies and preserve their low-frequency variations. Moreover, calculating the ageing curve and the final chronology by median, instead of mean, resulted in an amplified low-frequency climate signal. The results help to answer a growing need to better understand the behaviour of the sclerochronological data. In addition, we discuss the pitfalls that may potentially disrupt palaeoclimate signal detection in similar sclerochronological studies. Pitfalls may arise from shell taphonomy, water chemistry, time-variant characters of biological growth trends and small sample size.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".