Zoogeochemistry: Breaking Down the Silos Between Biogeochemistry and Zoology
Bibliographic record
Abstract
Abstract The field of zoogeochemistry focuses on including the effects of animals in ecosystem and biogeochemical concepts and computational models. Animals are known to have an effect disproportionate to their biomass on key ecosystem processes, such as a carbon and nitrogen cycling. However, it is challenging to include them into our models because we often lack mechanistic explanations behind animal effects, including how they affect the ecosystem structure and biogeochemistry. Jonsson et al. (2025, https://doi.org/10.1029/2024JG008598 ) report on a common garden experiment that provides a deeper mechanistic understanding of how nonnative earthworms impact soil carbon in the Scandinavian Arctic. They demonstrate that earthworms increased soil carbon in heath habitats by allowing plants to increase their rooting depth and root exudation. Conversely, they decreased soil carbon in meadow habitats by promoting the degradation of surface soil carbon. Their work is a compelling example of how to gather the mechanistic detail needed to incorporate animal effects into biogeochemical models.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".