Modeling carbon burial along the land to ocean aquatic continuum: Current status, challenges and perspectives
Bibliographic record
Abstract
Understanding the reallocation of atmospheric anthropogenic carbon (C) in the different compartments of the Earth System is a priority in Earth Science. Global numerical modeling of the C cycle stands as one of the fundamental tools for understanding how C cycles between the atmosphere, continents, and the ocean. However, Earth System Models and other large-scale models still lack a comprehensive depiction of the role of aquatic ecosystems along the Land-to-Ocean Aquatic Continuum (LOAC) in modulating organic carbon (OC) exchanges between terrestrial ecosystems and the ocean. The capacity of aquatic ecosystems to sequester organic carbon in the sediments they accumulate (i.e., organic carbon burial (OCB)) is a fundamental process for understanding the role of the LOAC in the global C cycle. Yet, the inclusion of this process into large-scale numerical models of the C cycle is still in its early stage. Here, we review the ecosystem processes involved in OCB along the LOAC and the terminology used by different authors, OCB measurement methodologies, the structure of large-scale C models, OCB rates available in the literature, and other data sources for modeling purposes. Our goal is to pinpoint the obstacles and potential solutions for incorporating OCB along the LOAC into Earth System Models and other large-scale applications. We identify the lack of language harmonization across different scientific disciplines working with ecosystems along the LOAC as a major caveat, and suggest a controlled vocabulary about OCB to assist addressing this challenge. We have compiled an updated global data set of OBC rates across ecosystems along the LOAC (lakes, reservoirs, floodplains, and coastal ecosystems), encompassing 1163 OCB rate values corresponding to 713 individual ecosystems, and showing strong biases in its distribution across the global geography and ecosystem types. We also show that virtually no existing large-scale C model incorporates OCB along the LOAC, although several have already made first steps towards the inclusion of this process at the global scale. Finally, we analyze the challenges and potential solutions to help paving the road for integrating OCB along the LOAC in large-scale models of the C cycle, including the pressing need for a multidisciplinary perspective in OCB modeling studies that brings together researchers from the several disciplines involved in the study of the ecosystems pertaining to the LOAC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 teacher head, 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".