Using End‐Member Models to Estimate Seasonal Carbonate Chemistry and Acidification Sensitivity in Temperate Estuaries
Bibliographic record
Abstract
Abstract We measured the carbonate system (between 2015 and 2018) in an isolated and a well‐connected temperate estuary, both known for shellfish growth. We evaluated end‐member model estimates of inorganic carbon, alkalinity, pH, mineral saturation states (Ωa), and pH sensitivity (βDIC). We find winter conditions are estimated within observational uncertainty. Spring‐summer primary productivity elevates observed pH and Ωa above theoretical lines, beyond uncertainty. Both estuaries are sensitive in winter and likely to experience rapid pH changes with increased inorganic carbon inputs. Summer pH sensitivity is reduced by productivity and is least sensitive in the midsalinity region. We estimate carbon increased by up to 49 μmol kg−1, since the pre‐industrial period resulting in significant decreases in pH (0.2) and Ωa (0.5). The largest pH decrease occurred outside the minimum buffer zone, at higher salinities where carbon increase was greatest. The largest pH decrease occurred in winter, but the largest Ωa decrease occured in summer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".