Elucidating anthropogenic impacts to soil organic matter composition and dynamics with integrative molecular biogeochemistry - <i>C.C. Patterson Medal Lecture</i>
Bibliographic record
Abstract
Soil organic matter is a critical component of terrestrial ecosystems because it is vital for maintaining soil fertility and overall ecosystem health. Soil organic matter is also a major global carbon sink with twice as much carbon stored in soils compared to CO 2 in the atmosphere. However, global environmental change and anthropogenic activities have impacted the intricate balance between soil carbon stored versus respired which is reducing soil carbon storage globally. Soil organic matter is a heterogeneous mixture of compounds from various plant and animal/microbial sources which participate in a range of biogeochemical reactions in soils. The complex geochemistry of soil organic matter is confounding our understanding of anthropogenic impacts, such as warming, increased nitrogen deposition and changes in carbon inputs, to terrestrial ecosystems. To circumvent these challenges, an integrative molecular-level platform has been developed that incorporates nuclear magnetic resonance spectroscopy along with targeted biomarker analysis via gas chromatography-mass spectrometry to better understand how soil organic matter chemistry is altered along with microbial drivers of substrate use. This presentation will provide an overview of these challenges, how these integrative approaches can provide a more informed assessment of biogeochemical processes as they relate to soil organic matter composition and microbial processing of available substrates. Results from several long-term ecological studies will be highlighted to exhibit how integrative molecular approaches foster a deeper understanding of soil carbon biogeochemical processes with global environmental change. These approaches are disentangling the complex processes related to soil organic matter reactivity and function and are paramount to developing a mechanistic and fundamental understanding of soil carbon stability in a changing world. This knowledge will in turn facilitate more informed mitigation practices for the protection of soil carbon resources around the globe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".