Estimating and Monitoring Dissolved Organic Carbon (DOC) Concentrations from Space across the Global Ocean
Bibliographic record
Abstract
Effective monitoring of Dissolved Organic Carbon (DOC) from space is crucial for tracking carbon stocks and understanding fluctuations in both coastal and open ocean environments. As part of the OCROC project, founded by the Copernicus Marine Service, we have enhanced the existing Ocean and Land Color Instrument (OLCI) DOC algorithm, designed to leverage data from both historical and current ocean color sensors. This algorithm initially used four key inputs: the absorption coefficient of Colored Dissolved Organic Matter (acdom), chlorophyll-a concentration, Sea Surface Temperature (SST), and Mixed Layer Depth (MLD), each at varied time lags to account for water mass dynamics.Our updated version introduces two optimized Artificial Neural Network models, a specialized approach for coastal areas affected by terrestrial influences, and reanalysis data for SST and MLD from COPERNICUS. We also assess the algorithm's adaptability for MODIS and VIIRS missions, requiring wavelength adjustments to ensure accurate acdom estimation—a process undergoing further calibration. By incorporating MODIS and VIIRS data, we aim to expand our dataset significantly, creating a new baseline for the algorithm.The final weekly DOC product, spanning from 1998 to 2022 with a 4 km resolution, is available for community evaluation and feedback. This dataset supports in-depth studies of DOC variations, the identification of significant spatial and temporal patterns, and long-term time series analyses and tendencies, thereby advancing our understanding of carbon dynamics in coastal and open ocean waters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".