Estimating Forest Soil Carbon and Nitrogen Stocks with Double Sampling for Stratification
Bibliographic record
Abstract
Precise and accurate estimation of C and N in forest soils is important for monitoring long‐term site productivity and C stock changes. Obtaining such estimates remains a major challenge, however, especially because of high natural variability in the forest floor. Although most researchers have used simple random sampling (SRS) for within‐plot soil sampling, double sampling for stratification (DSS) can be used to decrease costs, increase precision, and increase power. Estimates of C and N stocks based on DSS were compared with those estimated by SRS in the humus forms of Douglas‐fir [ Pseudotsuga menziesii (Mirb.) Franco] stands in the Cascade Mountains of Oregon. Generally, DSS was 1.34 to 5.11 times more efficient than SRS for total C, and 1.07 to 2.00 for total N. Coefficients of variation estimated from DSS were about one‐half of those estimated by SRS and reported elsewhere in the literature. The cost for sampling using DSS was one‐third to one‐half of that for SRS, depending on the number of strata used. Costs were reduced because fewer samples were required using DSS to provide the same precision as SRS. The DSS design was more powerful than SRS and could detect smaller changes than SRS with the same number of samples. Results suggested that the most efficient design for total C would use two strata where samples were allocated proportional to variance rather than proportional to area. Overall, large gains in efficiency can be realized with a more complex within‐plot sampling design, i.e., DSS, compared with SRS.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".