The GIEMS-MethaneCentric database: a dynamic and comprehensive global product of methane-emitting aquatic areas
Bibliographic record
Abstract
Abstract. The Global Inundation Extent from Multi-Satellites (GIEMS) database first published in 2001 was a key advance toward the accurate representation of wetlands globally by providing dynamic time series of global surface water based on passive microwave observations. This study supplements the second version of GIEMS (GIEMS-2) with other datasets to produce GIEMS-MethaneCentric (GIEMS-MC), a dynamically mapped dataset of methane-emitting waterlogged and inundated ecosystems. We separated open water from wetlands in GIEMS-MC by using the Global Lakes and Wetlands Database version 2 (GLWDv2) while adding unsaturated peatland areas undetected by GIEMS-2. Rice paddies are identified using the Monthly Irrigated and Rainfed Crop Areas (MIRCA2000) product. A specific coastal zone filtering is applied to avoid ocean artifacts while preserving coastal wetlands. GIEMS-MC covers the period 1992–2020 on a monthly scale at 0.25° × 0.25° spatial resolution. The GIEMS-MC product includes two layers of monthly wetland time series – one for flooded and saturated wetlands and another for all wetlands and peatlands – together with seven layers of compatible static maps of open water bodies (lakes, rivers, reservoirs) and seasonal rice paddy maps used in its production. The dominant vegetation and wetland types per pixel are also provided in GIEMS-MC variables. GIEMS-MC is compared to Wetland Area and Dynamics for Methane Modelling (WAD2M), a dataset providing dynamic wetland information. In terms of wetland extent, all wetlands and peatlands in GIEMS-MC and WAD2M show similar results, with a mean annual maximum of 7.7 Mkm2 for GIEMS-MC and 6.8 Mkm2 for WAD2M, with similar spatial patterns in most regions. The GIEMS-MC seamless time series represents a significant advance in wetland representation for methane modelling, although limitations remain in the accurate identification of rice, coastal, and peatland areas. This resource provides harmonized dynamic maps of aquatic methane-emitting surfaces and is available at https://doi.org/10.5281/zenodo.13919644 (Bernard et al., 2024a).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".