From laboratory manipulations to earth system models: predicting pelagic calcification and its consequences
Bibliographic record
Abstract
Abstract. The variation in pH-dependent calcification responses of coccolithophores paint a highly incoherent picture, particularly for the most commonly cultured "species", Emiliania huxleyi. The disparity between magnitude and even sign of the calcification change at higher CO2 (lower pH), raises challenges to quantifying future carbon cycle changes and feedbacks, by introducing significant uncertainty in parameterizations used for global models. Putting aside the possibility of methodological differences that introduce an experimental bias, we highlight two pertinent observations that can help resolve conflicting interpretations: (1) a calcification "optimum" in environmental conditions (pH) has been observed in other coccolithophore species, and (2) there exists an unambiguous direction to the CO2-calcification response across mesocosm and shipboard incubations. We propose that an equivalence can be drawn between integrated ecosystem calcification as a function of pH (or other carbonate system parameter such as calcite saturation state) and a widely used description of plankton growth rate vs. temperature – the "Eppley curve". This provides a conceptual framework for reconciling available experimental manipulations as well as a quasi-empirical relationship for ocean acidification impacts on carbonate production that can be incorporated into models. By analogy to the Eppley curve temperature vs. growth rate relationship, progressive ocean acidification in the future may drive a relatively smooth ecosystem response through transition in dominance from more to less heavily calcified coccolithophores in addition to species-specific calcification changes. However, regardless of the model parameterization employed, on a century time-scale, the CO2-calcification effect is a minor control of atmospheric CO2 compared to other C cycle feedbacks or to fossil fuel emissions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".