Field case studies of soil organic matter sequestration in Lithuania and the UK
Bibliographic record
Abstract
Investigations have assessed the environmental benefi ts of soil organic matter (SOM) storage at two long-term European experimental research sites: (i) SOM data from a soil conservation (set-aside) site in the UK and (ii) SOM data from a carbon sequestration benchmarking site in Lithuania. The fi rst case study (Hilton, UK) illustrates the environmental benefi ts of changes in SOM content before and after the adoption of set-aside, a recognized soil conservation technique. Ten run-off plots (7–15 ° gradients) were put to ley in 1991. Run-off and erosion rates decreased to tolerable levels once ~30 % vegetation cover had established and remained low (mean of 69 plot years 0.21 t ha–1 year–1, SD 0.14). Meanwhile, SOM content increased consistently and signifi cantly on the set-aside plots (mean of 2.22 % by weight in 14 years) and soil erodibility signifi cantly decreased. Results suggest using grass-leys for set-aside is a viable soil conservation technique, which may also contribute to carbon sequestration. The second case study (Kaltinenai, Lithuania) addresses the issue of comparing international SOM databases to assist carbon modelling and carbon sequestration estimates. Five analytical approaches have been used to calculate SOM. Linear correlation and paired regression equations were used to calculate the various techniques. Correlation coeffi cients varied between r = 0.83–0.98 (n = 92, P<0.001). Based on the strength and signifi cance of these relationships, it is proposed that simple linear or more complex
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".