Optimizing sampling allocation for detecting management effects on soil CO<sub>2</sub> emissions
Bibliographic record
Abstract
Measurement of soil CO2 flux is an important tool for detecting management induced changes in soil C. The objective of this study was to analyze sources of variability of a recently published CO2 flux dataset to identify a sampling protocol with optimal allocation of replication, sub-sample and treatment numbers for detecting treatment differences. The dataset comprised daily CO2 flux measurements from a long-term study with treatments of conventional tillage (CT) and no-till (NT) under continuous wheat (CONT) and fallow-wheat rotation (F-W) in a randomized complete block design (RCBD) with four blocks. PROC MIXED in SAS was used to estimate variances. The standard error of the difference (SED) between two treatment means was used as the precision indicator. Although increasing the number of replications effectively reduced SED, sub-sampling also often improved detection of treatment differences because sub-sample variance (σ2δ) was higher than experimental unit variance (σ2ε) on most sampling days. When treatments with small CT vs. NT difference were excluded, degrees of freedom for treatment effects were reduced and both variances were generally increased or unchanged, resulting in increased requirement for sub-sampling. Based on the selected dataset, we produced graphs showing the number of days on which a CT vs. NT difference of 0.3 µmol CO2 m-2 s-1 could be detected at P < 0.10 as a function of replication, sub-sample and treatment numbers. This approach may be used as a guide to optimize sample allocation in similar studies, though site- and experiment-specific factors (e.g., spatial and temporal variability of CO2 flux, size of treatment difference to be detected, the required confidence level) should also be considered. Key words: Carbon dioxide emissions, tillage, variance, wheat
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".