Net CO2 emissions from dry inland waters persist in the presence of vegetation
Bibliographic record
Abstract
This protocol outlines a standardized methodology for measuring CO2 fluxes from bare and vegetated dry sediments under both light and dark conditions. It provides a step-by-step procedure for estimating CO2 fluxes as well as sediment and vegetation characteristics in dry inland water bodies. The protocol describes methods and techniques for collecting and measuring sediment variables, including sediment temperature, moisture, total inorganic matter, organic matter, electrical conductivity, texture, pH, vegetation cover, and above-ground biomass. This work is part of the DRYFLUX-II initiative, supported by the Global Lake Ecological Monitoring Network (GLEON). The accompanying dataset provides an overview of the dry inland water bodies investigated to assess the influence of vegetation on CO2 emissions from dry beds. The study includes 164 inland water bodies, encompassing lakes, ponds, reservoirs, streams, and wetlands across a broad range of climatic regions, including tropical, arid, temperate, boreal, and polar zones. The dataset includes both in situ variables (e.g., sediment temperature, vegetation cover, mean annual temperature, and precipitation) and ex situ variables (e.g., sediment moisture, pH, electrical conductivity, and sediment texture). Additional metadata include the sampling country, water body type, weather conditions during sampling, dry-regime classification (chronically dry, ephemeral dry, intermittently dry, or temporarily dry), and the type of device used for CO2 measurements. Note: For questions or clarifications, please contact the corresponding authors. If this protocol or dataset is used for research purposes, please cite the dataset using the DOI provided by Zenodo.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.014 |
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".