Assessment of Porites microatolls for paleothermometry: Calibration for French Polynesia
Bibliographic record
Abstract
Massive dome-shaped coral Porites are the predominant choice for paleoclimate studies due to their consistent and reliable growth. When growing close to sea level, they become limited in their vertical growth and form so-called ‘microatolls’. Microatolls have not yet been extensively explored for paleoclimate reconstruction. Here, we investigate how reliable modern Porites microatolls are against empirical sea-surface temperature using Sr/Ca, δ 18 O, Li/Mg and Sr U paleothermometry methods on samples from the Society Islands, French Polynesia. Our results show Sr/Ca ratios have the lowest Standard Error of the Inverse Prediction (SEIP) at 0.415 °C ( N = 41) with a calibration of Sr/Ca (mmol mol −1 ) = −0.082 ± 0.006 SST (°C) + 11.256 ± 0.170 and with high reproducibility across multiple corals. The reproducibility of δ 18 O was less good, with SEIP increasing to 0.829 °C ( N = 41). Considering methods directly from the literature, Li/Mg ratio empirically corrected for Sr/Ca had the best balance between bias and precision where no local calibration could be available. This study independently evaluates and confirms the suitability of Porites microatolls from well-flushed environments for paleoclimate studies. Fossil dome-shaped Porites grow anywhere between near-surface and roughly 20 m depths which inherently incorporates uncertainty into any sea surface temperature reconstruction. This uncertainty is significantly reduced for microatolls due to their well-constrained bathymetry. The study represents a fundamental step in paleoclimate research targeting consistently near the water-air interface bringing reliability and, especially when combined with their ability to reconstruct past sea-level changes, microatolls have the potential to be central for future paleoenvironmental studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".