Climate, as well as branch-level processes, drive canopy soil abundance and chemistry
Bibliographic record
Abstract
Canopy soils can be important to forest nutrient cycling, hydrology, and biodiversity, but the factors determining their distribution and properties are largely unknown. We surveyed canopy soils across gradients of temperature and precipitation in six primary forests in Costa Rica. We used solid-state 13C nuclear magnetic resonance (NMR) and mass spectrometry (MS) to understand how the composition of canopy soil organic matter varies within and across sites. Climate, particularly fog, appears to drive canopy soil abundance across forests, while tree size determines canopy soil abundance within a forest. Canopy soil chemistry mostly varied within sites, though temperature was associated with the carbon (C) to nitrogen ratio, total dissolved nitrogen, and alkyl-C abundance, while fog explained some of the variation in dissolved organic carbon and O-alkyl C abundance. This study is the first-ever glimpse into large- and small-scale drivers of canopy soil abundance and biochemical composition. Our results highlight the importance of tree size and fog in determining the quantity and quality of canopy soil organic matter, suggesting that canopy soil stocks may be particularly vulnerable to climate and land use change. Identifying how multi-scale factors influence canopy organic matter processes will enhance our ability to identify and predict how environmental change might affect the abundance and chemistry of canopy soils and thus biodiversity that they support.
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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".