Photochemical Mineralization of Terrigenous DOC to Dissolved Inorganic Carbon in Ocean
Bibliographic record
Abstract
When terrigenous dissolved organic carbon (tDOC) rich in chromophoric dissolved organic matter (tCDOM) enters the ocean, solar radiation mineralizes it partially into dissolved inorganic carbon (DIC). This study addresses the amount and the rates of DIC photoproduction from tDOC and the area of ocean required to photomineralize tDOC. We collected water samples from 10 major rivers, mixed them with artificial seawater, and irradiated them with simulated solar radiation to measure DIC photoproduction and the photobleaching of tCDOM. The linear relationship between DIC photoproduction and tCDOM photobleaching was used to estimate the amount of photoproduced DIC from the tCDOM fluxes of the study rivers. Solar radiation was estimated to mineralize 12.5 ± 3.7 Tg C yr −1 (10 rivers) −1 or 18 ± 8% of tDOC flux. The irradiation experiments also approximated typical apparent spectral quantum yields for DIC photoproduction (ϕ λ ) over the entire lifetime of the tCDOM. Based on ϕ λ s and the local solar irradiances in river plumes, the annual areal DIC photoproduction rates from tDOC were calculated to range from 52 ± 4 (Lena River) to 157 ± 2 mmol C m −2 yr −1 (Mississippi River). When the amount of photoproduced DIC was divided by the areal rate, 9.6 ± 2.5 × 10 6 km 2 of ocean was required for the photomineralization of tDOC from the study rivers. Extrapolation to the global tDOC flux yields 45 (31–58) Tg of photoproduced DIC per year in the river plumes that cover 34 (25–43) × 10 6 km 2 of the ocean.
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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".