Controls on trace elements in stalagmites derived from in situ growth in a Chinese cave
Bibliographic record
Abstract
Stalagmites provide powerful archives for paleoclimate with potential for seasonal resolution and precise chronology.Most published stalagmite records have focused on 18 O as a proxy for past change, but 18 O responds to multiple variables, particularly temperature, rainfall source, and rainfall amount.Separating these effects to provide unambiguous information about the past environment can be difficult, and there is a need for additional proxies that provide complementary information and allow deconvolution of the controls on 18 O.Traceelement concentrations show significant variability on all timescales in stalagmites, but the controls on trace elements are complex and no reliable trace-element proxies have yet been developed.In this study we present data from drip waters and for calcites grown on glass plates under these drips to assess the controls on trace-element incorporation into stalagmites.The work was conducted in Heshang Cave in Central China, a site of existing paleoclimate work to reconstruct the Asian monsoon on millennial [1] to seasonal [2] timescales.Glass plates were replaced monthly for more than two years from January 2005 -a period that captures the unusually low summer rainfall of 2006 and therefore allows separation of the effects of temperature and drip-rate on stalagmite chemistry.The mass of carbonate grown on the plates peaks in the summer months, regardless of the year, indicating the dominant influence of temperature rather than drip rate on growth.Changes in drip-water chemistry are relatively small during the year in this cave, but carbonate trace-element concentrations vary reflecting temperature-dependant distribution coefficients, the mass of calcite precipitated, and the growth rate.These effects can be separated to derive understanding of trace-element incorporation into stalagmites.These results will be compared to those from other caves, and from other carbonate-precipitating systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".