Science AMA Series : We’re a team of researchers studying where wetlands can be found around the globe, from the arctic to the tropics, and trying to understand how human land use activities and climate change are affecting their distribution.
Bibliographic record
Abstract
Hi, we’re Drs. Ben Poulter (NASA), Thomas Gumbricht (CIFOR), David Olefeldt (University of Alberta) and Etienne Fluet-Chouinard (University of Wisconsin) — we study techniques to map wetlands around the world, how they change over time, and how this information can be used to understand how wetlands function and provide ecosystem services to people. Wetlands can be mapped using a variety of techniques, from sending people out into the field using inventory techniques to taking advantage of satellites in orbit around the Earth and using the electromagnetic spectrum. Recently, a new map of tropical wetlands was published by Thomas Gumbricht as well as a high-resolution map of global surface inundation by Etienne Fluet-Chouinard, both databases are being used for a variety of purposes, including to understand how wetland affect climate change by emitting methane. Join our AMA to find out how satellites are helping in the quest to learn more about where wetlands are located, how human activities affect wetland area, and how climate change is affecting methane emissions from wetlands. We’ll be back at 12 pm ET to answer your questions, AMA! Mapping tropical wetlands http://onlinelibrary.wiley.com/doi/10.1111/gcb.13689/full High-resolution global wetland mapping http://www.sciencedirect.com/science/article/pii/S0034425714004258 Understanding wetlands and methane emissions http://iopscience.iop.org/article/10.1088/1748-9326/aa8391/pdf
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.276 | 0.157 |
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".