In situ calcium carbonate dissolution in the Pacific Ocean
Bibliographic record
Abstract
Over the past several years researchers have been working to synthesize the WOCE/JGOFS global CO 2 survey data to better understand carbon cycling processes in the oceans. The Pacific Ocean data set has over 35,000 sample locations with at least two carbon parameters, oxygen, nutrients, CFC tracers, and hydrographic parameters. In this paper we estimate the in situ CaCO 3 dissolution rates in the Pacific Ocean water column. Calcium carbonate dissolution rates ranging from 0.01–1.1 μmol kg −1 yr −1 are observed in intermediate and deepwater beginning near the aragonite saturation horizon. In the North Pacific Intermediate Water between 400 and 800 m, CaCO 3 dissolution rates are more than 7 times faster than observed in middle and deep water depths (average = 0.051 μmol kg −1 yr −1 ). The total amount of CaCO 3 that is dissolved within the Pacific is determined by integrating excess alkalinity throughout the water column. The total inventory of CaCO 3 added by particle dissolution in the Pacific Ocean, north of 40°S, is 157 Pg C. This amounts to an average dissolution rate of approximately 0.31 Pg C yr −1 . This estimate is approximately 74% of the export production of CaCO 3 estimated for the Pacific Ocean. These estimates should be considered to be upper limits for in situ carbonate dissolution in the Pacific Ocean, since a portion of the alkalinity increase results from inputs from sediments.
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 | 0.000 | 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) | 0.000 | 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 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".