Quantification of organic carbon pools for Austria’s agricultural soils using a soil information system
Bibliographic record
Abstract
Within the framework of the project “Austrian Carbon Balance Model”, we estimated soil organic carbon (OC) content for the agricultural land of Austria. The basic chemical and physical data were obtained from the national electronic soil information system BORIS (Boden Rechnergestütztes Informtions System). The latter data were obtained through soil surveys performed over the past 10 yr. The BORIS data were corrected for soil gravel content, bulk densities and differences in chemical analytical methods used for soil OC. Our estimation also showed the following ranking for soil OC content (0–50 cm) under different land use systems: vineyards (57.6 t C ha -1 ) ~ cropland (59.5 t C ha -1 ) < orchards/gardenland (78 t C ha -1 ) ~ intensive grassland (81 t C ha -1 ) < extensive grassland (119 t C ha -1 ). Although the main portion of soil carbon is stored in topsoils (0–20 cm) in all land-use classes, deeper soil layers (20–50 cm) contribute significantly to the overall inventory (between 18. 2 and 27.2 t C ha -1 ), but appear to be less influenced by land use. A total OC storage in Austria’s agricultural soils of 284 Mt was estimated. A west-east gradient of OC storage in agricultural soils of different Federal Provinces was observed. Under Austrian conditions, extensively used grassland plays an important role for OC-storage. Wide C:N ratios in these soils suggest accumulation of poorly humified organic material and slow OC turnover. Key words: Carbon sequestration, soil organic matter, soil humus, soil nitrogen content, C:N ratio
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".