Annual fossil organic carbon delivery due to mechanical and chemical weathering of marly badlands areas
Bibliographic record
Abstract
ABSTRACT A key issue in the study of the carbon cycle is constraining the stocks and fluxes in and between C‐reservoirs. Among these, the role and importance of fossil organic carbon (FOC) release by weathering of outcropping sedimentary rocks on continental surfaces is still debated and remains poorly constrained. Our work focuses on FOC fluxes due to chemical and mechanical weathering of marls in two experimental watersheds with typical badlands geomorphology (Draix watersheds, Laval and Moulin, Alpes de Haute Provence, France). Organic matter from bedrock, soil litter and riverine particles are characterized by Rock‐Eval 6 pyrolysis. FOC fluxes due to mechanical weathering are then estimated by monitoring the annual particulate solid exports at the outlets of the watersheds (1985–2005 period). FOC fluxes from chemical weathering were calculated using Ca 2+ concentrations in dissolved loads (year 2002) to assess the amount of FOC released by the dissolution of the carbonate matrix. Results show that FOC delivery is mainly driven by mechanical weathering, with a yield ranging from 30 to 59 t km ‐2 yr ‐1 in the Moulin (0.08 km 2 ) and Laval (0.86 km 2 ) catchments, respectively, (1985–2005 average). The release of FOC attributed to chemical weathering was 2.2 to 4.2 t km ‐2 for the year 2002. These high FOC fluxes from badlands are similar to those observed in tectonically active mountain catchments. At a regional scale, badland outcropping within the Durance watershed does not exceed 0.25% in area of the Rhône catchment, but could annually deliver 12 000 t yr ‐1 of FOC. This flux could correspond to 27% of the total particulate organic carbon (POC) load exported by the Rhône River to the Mediterranean Sea. At a global scale, our findings suggest that erosion of badlands may contribute significantly to the transfer of FOC from continental surfaces to depositional environments. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 | 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) | 0.000 | 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".