Seasonality and Budgets of Soil Greenhouse Gas Emissions From a Tropical Dry Forest Successional Gradient in Costa Rica
Bibliographic record
Abstract
Abstract Limited information on greenhouse gas emissions from tropical dry forest soils still hinders the assessment of the sources/sinks from this ecosystem and their contribution at global scales. Particularly, rewetting events after the dry season can have a significant effect on soil biogeochemical processes and associated exchange of greenhouse gases. This study evaluated the temporal variation and annual fluxes of CO 2 , N 2 O, and CH 4 from soils in a tropical dry forest successional gradient. After a prolonged drought of 5 months, large emissions pulses of CO 2 and N 2 O were observed at all sites following first rain events, caused by the “Birch effect,” with a significant effect on the net ecosystem exchange and the annual emissions budget. Annual CO 2 emissions were greatest for the young forest (8,556 kg C ha −1 yr −1 ) followed by the older forest (7,420 kg C ha −1 yr −1 ) and the abandoned pasture (7,224 kg C ha −1 yr −1 ). Annual emissions of N 2 O were greatest for the forest sites (0.39 and 0.43 kg N ha −1 yr −1 ) and least in the abandoned pasture (0.09 kg N ha −1 yr −1 ). CH 4 uptake was greatest in the older forest (−2.61 kg C ha −1 yr −1 ) followed by the abandoned pasture (−0.69 kg C ha −1 yr −1 ) and the young forest (−0.58 kg C ha −1 yr −1 ). Fluxes were mainly influenced by soil moisture, microbial biomass, and soil nitrate and ammonium concentrations. Annual CO 2 and N 2 O soil fluxes of tropical dry forests in this study and others from the literature were much lower than the annual fluxes in wetter tropical forests. Conversely, tropical dry forests and abandoned pastures are on average stronger sinks for CH 4 than wetter tropical forests.
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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 | 0.001 |
| 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) | 0.000 | 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".