Foliar methane and nitrous oxide flux: patterns and drivers in temperate tree species
Bibliographic record
Abstract
The dataset comprises repeated in-situ measurements of foliar and soil gas fluxes, as well as leaf and soil elemental traits, for 25 temperate tree species interplanted at a forest restoration site. Its primary purpose is to quantify species-specific foliar contributions to atmospheric methane (CH₄), nitrous oxide (N₂O), carbon dioxide (CO₂), and water vapor (H₂O) fluxes, and to explore how these fluxes relate to leaf traits, soil properties, and plant functional strategies. The dataset contains 500 observations across 31 variables, including species identity (common and scientific names), taxonomic family, functional traits such as shade, drought, and waterlogging tolerance, and leaf type (deciduous or evergreen). Individual trees and leaves are uniquely identified, and measurements were collected across multiple seasons. Foliar variables include CH₄, N₂O, CO₂, and H₂O fluxes, as well as leaf carbon and nitrogen content, while soil variables include corresponding gas fluxes and soil carbon and nitrogen concentrations. Additionally, leaf structural traits such as specific leaf area (SLA) are included to facilitate trait-based analyses. This dataset enables comprehensive analyses of interspecific and seasonal variation in foliar greenhouse gas exchange, the mechanistic links to microbial and physiological traits, and the contribution of temperate tree foliage to ecosystem-level greenhouse gas budgets. It provides both the breadth (25 species) and depth (multiple leaves per tree, repeated measurements across seasons) required to explore functional relationships between plant traits, environmental tolerance strategies, and foliar gas fluxes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".