<i>Porites</i> Calcifying Fluid pH on Seasonal to Diurnal Scales
Bibliographic record
Abstract
Abstract Coral resilience to ocean acidification is largely determined by the degree of physiological control corals can exert on their calcifying fluid carbonate chemistry. In this study, the boron isotopic composition (δ11B) of a Porites colony growing on a reef flat on Kiritimati Island in the equatorial central Pacific is examined to quantify the sensitivity of calcifying fluid pH (pHcf) to ambient environmental conditions. Skeletal δ11B along the growth axis of one annual growth band was determined with bulk analysis and by laser ablation (LA) MC‐ICP‐MS. Furthermore, the oxygen and carbon isotopic composition, trace element ratios, and skeletal density were quantified. Sclerochronological data were interpreted in the context of simultaneous recordings of reef flat seawater pH (pHsw), temperature, salinity, and water depth, and by measurements of these parameters on the fore‐reef. A recent model of pHcf upregulation, after optimization with seasonally resolved data, was used to simulate pHcf variability on a diurnal scale. Results showed that on a seasonal scale, Porites pHcf is upregulated compared to ambient seawater: both bulk and LA‐MC‐ICP‐MS derived δ11B resulted in a mean pHcf of 8.35 pH units. Calcifying fluid pH upregulation primarily followed variations in seawater temperatures, that is likely related to the control of temperature on calcification rate. On the reef flat, the diurnal range in pHsw was substantially higher (0.29 pH units) than on the fore‐reef (0.07 pH units). However, model results suggest that the high diurnal variability in reef flat pHsw resulted only in a limited variability in Porites pHcf.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".