The renaissance of Odum's outwelling hypothesis in 'Blue Carbon' science
Bibliographic record
Abstract
The term ‘Blue Carbon’ was coined about a decade ago to highlight the important carbon sequestration capacity of coastal vegetated ecosystems. The term has paved the way for the development of programs and policies that preserve and restore these threatened coastal ecosystems for climate change mitigation. Blue carbon research has focused on quantifying carbon stocks and burial rates in sediments or accumulating as biomass. This focus on habitat-bound carbon led us to losing sight of the mobile blue carbon fraction. Oceans, the largest active reservoir of carbon, have become somewhat of a blind spot. Multiple recent investigations have revealed high outwelling (i.e., lateral fluxes or horizontal exports) of dissolved inorganic (DIC) and organic (DOC) carbon, as well as particulate organic carbon (POC) from blue carbon habitats. In this paper, we conceptualize outwelling in mangrove, saltmarsh, seagrass and macroalgae ecosystems, diagnose key challenges preventing robust quantification, and pave the way for future work integrating mobile carbon in the blue carbon framework. Outwelling in mangroves and saltmarshes is usually dominated by DIC (mostly as bicarbonate), while POC seems to be the major carbon species exported from seagrass meadows and macroalgae forests. Carbon outwelling science is still in its infancy, and estimates remain limited spatially and temporally. Nevertheless, the existing datasets imply that carbon outwelling followed by ocean storage is relevant and may exceed local sediment burial as a long-term (>centuries) blue carbon sequestration mechanism. If this proves correct as more data emerge, ignoring carbon outwelling may underestimate the perceived sequestration capacity of blue carbon ecosystems.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".