A global database of measured values of Li/Mg, Mg/Ca, Sr/Ca, Ba/Ca, U/Ca and Sr-U for coral and coralline algae paleoenvironment calibrations
Bibliographic record
Abstract
Calcifying marine organisms provide a valuable means to access high-resolution historical records of the marine environment captured within their skeletal geochemistry. These records are essential for comprehending the effects of human-induced climate change and reducing uncertainties in future projections. Integrating investigations across various taxa, depths, and geographic locations can help identify universal environmental proxies and serve as a basis for targeted studies in the future. Here, we provide a comprehensive georeferenced database of measured values of Li/Mg, Mg/Ca, Sr/Ca, Ba/Ca, U/Ca and Sr-U in coral and coralline algae compiled from the scientific literature (1950-2021; http://www.webofknowledge.com, accessed 2022-09-30) for the purpose of interrogating and refining global, mineralogy specific and/or taxon-specific proxies for seawater temperature and barium. We include metadata relating to the source, timing and location of each study, the methodology used, and environmental and experimental information. The dataset presents an opportunity to quantify uncertainty and test the robustness of trace and minor element proxies for past environmental conditions, of which will be of value within the fields of geochemistry, ecology, climate, and palaeobiology. Full methodology and additional information provided in Williams et al. (2024).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".