Dynamics of microbial community composition and soil organic carbon mineralization in soil following addition of pyrogenic and fresh organic matter
Bibliographic record
Abstract
Abstract Pyrogenic organic matter (PyOM) additions to soils can have large impacts on soil organic C (SOC) cycling. Because the soil microbial community drives SOC fluxes, understanding how PyOM additions affect soil microbes is essential to understanding how PyOM affects SOC. We studied SOC dynamics and surveyed soil microbial communities after OM additions in a field experiment. We produced and applied either 350°C corn stover PyOM or an equivalent amount of dried corn stover to a Typic Fragiudept soil. Stover increased SOC-derived and total CO 2 fluxes (up to 6x), and caused rapid and persistent changes in bacterial community composition over 82 days. In contrast, PyOM only temporarily increased total soil CO 2 fluxes (up to 2x) and caused fewer changes in bacterial community composition. 70% of the OTUs that increased in response to PyOM additions also responded to stover additions. These OTUs likely thrive on easily-mineralizable C that is found both in stover and, to a lesser extent, in PyOM. In contrast, we also identified unique PyOM-responders, which may respond to substrates such as polyaromatic C. In particular, members of Gemmatimonadetes tended to increase in relative abundance in response to PyOM but not to fresh organic matter. We identify taxa to target for future investigations of the mechanistic underpinnings of ecological phenomena associated with PyOM additions to soil.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".