Tracing deep‐sea calcite dissolution: Agreement between the <i>Globorotalia menardii</i> fragmentation index and elemental ratios (Mg/Ca and Mg/Sr) in planktonic foraminifers
Bibliographic record
Abstract
Accurately quantifying deep‐sea calcite dissolution is crucial for understanding the role of the marine carbonate system in regulating atmospheric pCO2 over millennia. We compare a foraminifer‐fragmentation‐based calcite dissolution proxy (Globorotalia menardii fragmentation index (MFI)) to Mg/Ca, Sr/Ca, and Mg/Sr in several species of deep dwelling planktonic foraminifers. We conducted microfossil and geochemical analyses on the same core top samples taken at different depths on the Ontong Java Plateau to maximize the dissolution signal and minimize the temperature overprint on our data. We also compare elemental ratios from planktonic foraminifer tests to modern bottom water CO3= undersaturation and model‐derived estimates of percent calcite dissolved in deep‐sea sediments. We find clear linear decreases in Mg/Ca or Mg/Sr in G. menardii and Pulleniatina obliquiloculata with increasing (1) bottom water CO3= undersaturation, (2) percent calcite dissolved in sediments calculated with biogeochemical modeling, (3) MFI, and (4) percent calcite dissolved derived from MFI. These findings lend further support to MFI as a calcite dissolution proxy for deep‐sea sediments. In contrast, we find no significant correlation between Sr/Ca and independent dissolution indicators. Our results suggest that Mg/Ca and Mg/Sr from deep dwelling foraminifers could potentially be used as calcite dissolution proxies in combination with independent water temperature estimates. Likewise, establishing the relationship between MFI and dissolution‐induced changes in the Mg/Ca of surface‐dwelling foraminifers could provide a tool to correct Mg/Ca–derived sea surface temperature reconstructions for calcite dissolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".