A metagenomic survey of forest soil microbial communities more than a decade after timber harvesting
Bibliographic record
Abstract
The scarcity of long-term data on soil microbial communities in the decades following timber harvesting limits current understanding of the ecological problems associated with maintaining the productivity of managed forests. The high complexity of soil communities and the heterogeneity of forest and soil necessitates a comprehensive approach to understand the role of microbial processes in managed forest ecosystems. Here, we describe a curated collection of well replicated, multi-faceted data from eighteen reforested sites in six different North American ecozones within the Long-term Soil Productivity (LTSP) Study, without detailed analysis of results or discussion. The experiments were designed to contrast microbial community composition and function among forest soils from harvested treatment plots with varying intensities of organic matter removal. The collection includes 724 bacterial (16S) and 658 fungal (ITS2) amplicon libraries, 133 shotgun metagenomic libraries as well as stable isotope probing amplicon libraries capturing the effects of harvesting on hemicellulolytic and cellulolytic populations. This collection serves as a foundation for the LTSP Study and other studies of the ecology of forest soil and forest disturbance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".