Assessment of a Method to Measure Temporal Change in Soil Carbon Storage
Bibliographic record
Abstract
Sensitive methods are essential to resolve small changes in soil C storage, such as those attained in sequestration projects, against much larger quantities of C already present. To measure temporal changes in C storage we proposed a high‐resolution method based on collecting volumetric soil cores from a microsite (4 by 7 m), marking core locations to intersperse multiple cores collected initially and in a subsequent sampling year, rigorous analytical quality control, and calculating soil C pool sizes with proper corrections for unequal soil masses. To evaluate the method, we measured the recovery of 3.64 Mg C ha −1 added as coal dust to microsites. We calculated C stored in successive soil layers of both fixed volume and equivalent mass. We inferred coal C recovery from spatial comparisons between coal‐amended and unamended plots, and from temporal comparisons between soil samples collected before and after coal addition. The comparisons among C storage showed effective recovery of added coal C, but only for paired temporal differences based on calculations of organic C storage in an equivalent soil mass. With spatial comparisons, coal C became undetectable when soil thickness exceeded 35 cm. With temporal comparisons, coal C recovery ranged from 91 to 106%, provided differences were calculated for successively thicker layers of equivalent soil mass. In contrast, recovery was only 64 to 82% when temporal differences were calculated for layers of fixed soil volume. The method is useful to quantify small temporal changes in soil organic C storage within microsites, and possibly over more extensive areas with sufficient samples to characterize spatial variability.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".