Satellite observation of particulate organic carbon dynamics on the <scp>L</scp>ouisiana continental shelf
Bibliographic record
Abstract
Particulate organic carbon (POC) plays an important role in coastal carbon cycling and the formation of hypoxia. Yet, coastal POC dynamics are often poorly understood due a lack of POC observations and the complexity of coastal hydrodynamic and biogeochemical processes that influence POC sources and sinks. Using a dataset of field observations and satellite ocean color products, we developed a new multiple regression algorithm to derive POC from satellite observations in two river-dominated estuaries in the northern Gulf of Mexico: the Louisiana Continental Shelf (LCS) and Mobile Bay. The algorithm had reliable performance with mean relative error (MRE) of ~40%, and root mean square error (RMSE) of ~50% for MODIS and SeaWiFS images in the two systems. Substantial spatio-temporal variability was observed from satellite on the LCS, with higher POC on the inner shelf (< 10 m depth) and lower POC on the middle (10-50 m depth) and outer shelves (50-200 m depth), and with higher POC in winter (January to March), and lower POC in summer to fall (August to October). Correlation analysis between long-term POC time series and several potential influencing factors indicated that river discharge dominants POC dynamics on the LCS. Wind and surface currents also affect POC spatial patterns on short time scales. This study demonstrates that algorithms that can determine coastal POC from satellites greatly increase the spatial and temporal extent of observations available for characterizing POC dynamics and their relations to various dominant physical forcings to the continental shelf and estuaries.
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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 | 0.001 |
| 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".