Ex situ soil respiration assessment using minimally disturbed microcosms and dried–sieved soils; comparison of methods to assess soil health
Bibliographic record
Abstract
Soil respiration measurements are commonly used as soil health indicators. Several ex situ soil respiration methods exist, but comparative performances between them have rarely been analyzed. Specifically, there is a lack of comparisons between intact microcosms and destructive methods. The objective of this study was to analyze and compare three different ex situ soil respiration methodologies: minimally disturbed microcosms using fresh soil, dried–sieved 24 h burst test, and dried–sieved 10-day incubation. We hypothesized that ( i) the respiration rates for the three methods are correlated to each other; ( ii) the respiration rates are strongly correlated with soil physico-chemical parameters; ( iii) disturbance caused by drying and sieving reduces regression coefficients compared with microcosms; and ( iv) drying and sieving soil produces larger respiration rates. Soil was collected in the Province of New Brunswick, Canada. Total carbon and nitrogen (C:N), pH, aggregate stability, total dissolved C and N, NO3 and NH4, texture, and labile C were determined prior to incubations. Our results showed that the three methods had CO2 efflux in similar ranges. However, all the methods had low to no significant correlations between soil physico-chemical parameters and respiration. Total dissolved N had the strongest correlation with CO2 efflux. The results of the microcosm method significantly correlated with the results for 24 h burst test but not with the 10-day incubation method. We conclude that drying and sieving soil prior to performing ex situ soil heterotrophic respiration measurements using the 24 h burst tests can produce cautiously reliable results. Despite the disturbance, results from the 24 h burst tests are comparable with the results of the microcosm method.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".