Small root exclusion collars provide reasonable estimates of root respiration when measured during the growing season of installation
Bibliographic record
Abstract
A common method to determine in situ root respiration is to insert root exclusions to sever roots and then to measure soil carbon dioxide (CO2) efflux in and outside the exclusion. We report the use of relatively small root exclusions (15.2 cm diameter plastic pipe) (SREs), installed and measured within a growing season. We switched from long-used, large root exclusions (2.5 m × 3 m) (LREs) for three reasons. First, temperature artifacts were apparent in LREs, likely because increased soil moisture altered soil thermal balance. Second, LREs in dense stands required a relatively low tree density, which then impacted snowpack depth and insolation. Third, the LREs were much more time-consuming to install than SREs. Using a powered mechanical trencher (ditch witch®) decreased LRE installation time, but introduced a large edge-effect apparent in soil profile pCO2 that would obviate trenched plots smaller than 1600 cm2. However, when trenches were dug by hand, the distance from the LRE wall had no effect on soil pCO2. In a subsequent experiment, SREs were installed by cleanly cutting the forest floor, and then immediately measured. Within 1-3 weeks the SREs provided similar root respiration estimates to those made with LREs that had been in place for nearly 10 months. SREs placed in and outside LREs provided indistinguishable microbial respiration values from one another and to the LREs. We conclude SREs provide root respiration estimates indistinguishable from other methods, even when installed and measured within the same growing season.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".