Community Insights for scoping a NASA Terrestrial Ecology field campaign in drylands: ARID
Bibliographic record
Abstract
Dryland ecosystems cover 40% of our planet’s land surface, support the lives of billions of people, and are responding dramatically to the combined effects of climate and land use change. These expansive and diverse systems also dominate core aspects of Earth’s climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite the indispensable natural resources and ecosystem services provided by drylands and their high vulnerability to change, drylands are one of the most, if not the most, poorly understood ecosystem types. Such lack of study has been in part due to incorrect historical assumptions that drylands are unproductive “wastelands”. This lack of understanding results in notably poor model representation and forecasting capacity, hindering our representation and decision making for these vulnerable ecosystems. The NASA Terrestrial Ecology Program solicited proposals for a multi-year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. With the goal of gathering input from the scientific and data end-user communities, we provide an overview of our ARID kick-off meeting with over 300 in-person and virtual participants held in October 2023 at the University of Arizona. This meeting gathered insights from public and private data end-users and scientists. We also report on follow-up activities that have taken place since then, including town halls, community surveys, and international engagements.
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 | 0.036 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".