Dinosterols or dinocysts to estimate dinoflagellate contributions to marine sedimentary organic matter?
Bibliographic record
Abstract
Dinosterol (4α,23,24‐trimethyl‐5α‐cholest‐22E‐en‐3β‐ol) is frequently used as an alternative to dinoflagellate cyst (dinocyst) counting in paleoceanography to assess dinoflagellate inputs to marine sediments. However, recent studies have shown poor correlation between these two proxies in continental‐margin sediments. We reevaluated the relationship and expanded it to include a suite of biogeochemical transformation products of the parent dinosterol (dinosterone, dinostanone, and dinostanol). These dinoflagellate‐specific 4α,23,24‐trimethyl steroidal species (𐎣dinosterol) are compared to dinocyst counts in sediments from the western Mexican margin (375–3,500 m). Samples were taken from subsurface (3‐6 cm) and down core (16‐27 cm) to reflect widely contrasting organic carbon content and redox conditions. A strong correlation was found between the sum of all dinoflagellate‐derived sterols, 𐎣dinosterols, and total dinocyst counts, highlighting the importance of including diagenetic alteration products of the parent molecule when exploiting organic biomarkers in paleoceanographic studies. In low‐energy environments and for well‐preserved samples, such as those studied in this work, both methods provide robust, internally consistent data, suggesting that when diagenetic transformation products of dinosterol are taken into account, gas chromatography and optical microscopy could be used interchangeably to estimate dinoflagellate inputs to marine sediments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".