The dynamic ocean acidification manipulation experimental system: Separating carbonate variables and simulating natural variability in laboratory flow‐through experiments
Bibliographic record
Abstract
Abstract Carbonate chemistry variables such as PCO 2 , pH, and mineral saturation state ( Ω ) are commonly thought of as covarying in open‐ocean settings but have decoupled over geologic time‐scales and among modern dynamic coastal margins and estuaries. Predicting responses of vulnerable coastal organisms to past, present, and future ocean acidification (OA) scenarios requires the empirical identification of organismal sensitivity thresholds to individual carbonate chemistry parameters. Conversely, most OA experiments involve chemistry manipulations that result in covariance of carbonate system variables. We developed the Dynamic Ocean Acidification Manipulation Experimental System (DOAMES)—a feed‐forward, flow‐through carbonate chemistry control system capable of decoupling PCO 2 , pH, or Ω by independently manipulating total alkalinity (TAlk) and total inorganic carbon (TCO 2 ). DOAMES proof‐of‐concept can manipulate source seawater with stable or variable carbonate chemistry and produce experimental treatments with constant and dynamic carbonate chemistry regimes. The combination of dynamic input and output allows for offset treatments that impose a ΔPCO 2 on naturally variable conditions. After overcoming several operational challenges, DOAMES is capable of simultaneously generating three different experimental treatments within 1% ± 1% of TCO 2 and TAlk targets. The achieved precision and accuracy resulted in the successful decoupling of pH and Ω Ar in five trials. We tested the viability of sensitive bivalve embryos raised in DOAMES‐manipulated seawater and found no difference in development when compared to the control, demonstrating DOAMES suitability for organismal studies. DOAMES provides a novel tool to evaluate organismal effects of exposure to decoupled carbonate system variables and to past, current, and future carbonate chemistry scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".