A Method for Determining Community Level Physiological Profiles of Organic Soil Horizons
Bibliographic record
Abstract
Community level physiological profiles (CLPPs) have been widely used to assess microbial community diversity in soils. Refinement of techniques to determine soil CLPP eventually led to the development of the MicroResp technique. This technique avoids many of the pitfalls of earlier methods by using a whole-soil approach coupled with the convenience of microplates. However, issues related to soil pretreatment (primarily sieving) can arise when using the standard MicroResp method to determine the CLPPs of forest floors. Here, we developed a modified multiple substrate induced respiration (multi-SIR) method that lessens the effects of pretreatment by using a larger soil volume in custom 24-well deep-well plates. Microbial community indices including catabolic evenness (E) and CLPP were determined on a range of forest soils using both the standard MicroResp and our modified multi-SIR method. The modified method reduced the variation among the triplicate substrate wells and displayed a wider range of E among the soils measured. Additionally, using multivariate nonmetric multidimensional scaling (NMDS) as well as cluster analysis, we found that the modified method was able to better detect differences in soil CLPPs. The standard MicroResp method remains a valuable technique for many soils; however, our modified multi-SIR method is more suitable for organic soils, such as forest floors, that have low bulk density.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.001 |
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".