Fine resolution map of top- and subsoil carbon sequestration potential in France
Bibliographic record
Abstract
Although soils have a high potential to offset CO2 emissions through its conversion into soil organic carbon (SOC) with long turnover time, it is widely accepted that there is an upper limit of soil stable C storage, which is referred to SOC saturation. In this study we estimate SOC saturation in French topsoil (0–30 cm) and subsoil (30–50 cm), using the Hassink equation and calculate the additional SOC sequestration potential (SOCsp) by the difference between SOC saturation and fine fraction C on an unbiased sampling set of sites covering whole mainland France. We then map with fine resolution the geographical distribution of SOCsp over the French territory using a regression Kriging approach with environmental covariates. Results show that the controlling factors of SOCsp differ from topsoil and subsoil. The main controlling factor of SOCsp in topsoils is land use. Nearly half of forest topsoils are over-saturated with a SOCsp close to 0 (mean and standard error at 0.19 ± 0.12) whereas cropland, vineyard and orchard soils are largely unsaturated with degrees of C saturation deficit at 36.45 ± 0.68% and 57.10 ± 1.64%, respectively. The determinant of C sequestration potential in subsoils is related to parent material. There is a large additional SOCsp in subsoil for all land uses with degrees of C saturation deficit between 48.52 ± 4.83% and 68.68 ± 0.42%. Overall the SOCsp for French soils appears to be very large (1008 Mt C for topsoil and 1360 Mt C for subsoil) when compared to previous total SOC stocks estimates of about 3.5 Gt in French topsoil. Our results also show that overall, 176 Mt C exceed C saturation in French topsoil and might thus be very sensitive to land use change.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".