Upslope length improves spatial estimation of soil organic carbon content
Bibliographic record
Abstract
Quantifying soil organic carbon (SOC) is important to aide in assessing carbon (C) sequestration potential, and as an indicator of soil quality. However, intensive s ampling of SOC for quantification can be expensive and time consuming. The objectives of this study were to identify which topographic index correlated best with SOC and determine if incorporating the index improved interpolation of limited SOC data. A transect with 93 sample points spaced 6 m apart was set up, and four topographical indices (curvature, wetness index, upslope length, and elevation) were evaluated for their potential as secondary variables. Three Kriging-based interpolation methods, ordinary kriging, cokriging, and simple kriging with varying local means were compared to determine if incorporating topographical indices improved interpolation of SOC. The upslope length, which takes into consideration the quantity of water that will be redistributed to a point, was found to have the strongest relationship with SOC (R 2 = 0.48, P < 0.01) and was used as a secondary variable for kriging. Thirty points from the SOC data were randomly selected and used in the kriging algorithms to estimate the remain ing 63 points. The sum of squared differences (SSD) showed a significant reduction (from 1677 to 1455 for SKlm and from 1677 to 1464 for cokriging) in estimates when upslope length was used as a secondary variable. These results indicate that fewer samples may be taken to estimate SOC accurately and precisely if upslope length is incorporated. On a landscape scale this could facilitate quantification of carbon credits and management decisions in precision farming systems. Key words: Geostatics, kriging, cokriging, organic carbon, landscape processes, wetness index
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".