Sample storage-induced changes in the quantity and quality of soil labile organic carbon
Bibliographic record
Abstract
Effects of sample storage methods on the quantity and quality of labile soil organic carbon are not fully understood even though their effects on basic soil properties have been extensively studied. We studied the effects of air-drying and frozen storage on cold and hot water soluble organic carbon (WSOC). Cold- and hot-WSOC in air-dried and frozen-stored soils were linearly correlated with those in fresh soils, indicating that storage proportionally altered the extractability of soil organic carbon. Air-drying but not frozen storage increased the concentrations of cold-WSOC and carbohydrate in cold-WSOC, while both increased polyphenol concentrations. In contrast, only polyphenol concentration in hot-WSOC was increased by air-drying and frozen storage, suggesting that hot-WSOC was less affected by sample storage. The biodegradability of cold- but not hot-WSOC was increased by air-drying, while both air-drying and frozen storage increased humification index and changed specific UV absorbance of both cold- and hot-WSOC, indicating shifts in the quality of soil WSOC. Our results suggest that storage methods affect the quantity and quality of WSOC but not comparisons between samples, frozen storage is better than air-drying if samples have to be stored, and storage should be avoided whenever possible when studying the quantity and quality of both cold- and hot-WSOC.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".