Tephra compositional data: are we doing it right?
Bibliographic record
Abstract
Tephrochronology is often used for dating of natural archives and for correlation between study sites. Volcanic ashes (tephra) extracted from cores used for climate reconstructions function as common time-marker horizons and become anchor points in comparisons of age models from different studies. Given these uses, tephrochronology is well placed to help overcome the chronological challenges that hinder sufficiently precise dating of palaeoclimate records.Tephras are identified based on their geographic and stratigraphic contexts, glass shard morphology, and geochemical composition. The geochemistry is most commonly analysed by electron probe microanalyser and presented as weight percentages of oxides of the nine or ten most abundant elements, often normalised to a 100 % total for ease of comparison. A simple exploration of such results and comparison to published data of previous tephra findings is usually enough for confident identification. However, compositional data of tephra findings from new studies continuously add to the complexity of tephrochronological investigations by increasing the amount of data available for comparison, including the addition of new potential candidates for identification. The increased likelihood of multiple candidates—sometimes with overlapping geochemistries—means that statistical data analyses are increasingly necessary.Tephrochronologists have used principal component analysis and discriminant function analysis in situations needing statistical approaches, but these methods’ validity often requires certain assumptions not to be violated. A rarely considered example of such an issue is that compositional data suffers from the constant-sum constraint and must be converted by log-ratio transformations for some statistical analyses to function properly. As there is presently no consensus on a tephra compositional data curation procedure including log-ratio transformations, we have explored several variations and compared the results to see if a formal recommendation for such a procedure is relevant for the tephra community.
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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 | 0.038 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
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".