Variations in Soil Carbon Stocks with Texture and Previous Landuse in North-western NSW, Australia
Bibliographic record
Abstract
<p>Australia’s land managers will need specific information about the best locations at which to sequester carbon if they are to take advantage of the recent Carbon Farming Initiative of the Australian Government under which carbon offsets can be created through sequestration of carbon in soil and trees. The literature indicates that soil texture and previous landuse are important determinants of soil carbon content. This paper describes the results of work to assess the current levels of soil carbon and the extent to which they vary with previous landuse and soil texture in the Namoi Catchment Management Authority in North West NSW, Australia. Soil samples were taken at 74 sites for determination of soil carbon concentration and stocks as well as soil texture and landuse in the last 10 years. There was wide variation between sites in soil carbon concentration and stocks which were greatest in those soils which had not been disturbed by cultivation and in soils with higher clay content. Thus, the greatest potential for carbon sequestration is in soils with the lowest carbon concentration, those which have been previously disturbed, and with higher clay content. Maintaining any increased carbon concentration will depend on minimizing disturbance, increased carbon input and minimizing loss of carbon through soil erosion. As these factors all vary significantly on a regional and landscape basis it will be important for land managers to have access to information which allows them to choose the sites at which potential for sequestration of soil carbon is greatest.</p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".