Compilation of data on soil organic carbon stocks and N2O emissions from published long term fields trials
Bibliographic record
Abstract
This dataset was produced within the Landmark project to assess the accuracy of the DEXI climate regulation model. This was done by simulating the response of the soil organic carbon stock or N2O emissions from agricultural soils in long-term experiments (LTEs) and comparing the model outcomes to reported changes. LTEs were chosen because they facilitate the assessment of a range of different management practices on the component parts of the climate regulation function over a decadal timescale. The geographical location of the LTEs was limited to Europe, since the model has been developed specifically to make simulations across Europe. For this purpose, the database constructed by Sandén et al. (2018) was used. This database contains publications on 251 European LTEs in which the effect of alternative management practices on soil quality were assessed. From these, 78 LTEs reported on changes in SOC stocks and 40 reported on changes in N2O emission or NO3- leaching. A large portion of these LTEs studied the effect of tillage (n = 18 for N2O, n = 33 for C stocks). As the effect of tillage on these soil properties has been summarized in multiple meta-studies, it was chosen not to run all these studies separately by the model, but instead, model performance was assessed based on these meta-analyses (Luo et al., 2010; Powlson et al., 2014). After excluding studies on the effect of tillage and studies using parameters that are not simulated by the model, the number of studies we retained to test the model was 6 for N2O emissions, 2 for NO3- leaching and 12 for changes in SOC stocks. This includes one additional study on NO3- leaching (Hansen and Djurhuus, 1997) and one on C sequestration (Spiegel et al., 2018) that were added to the dataset.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".